Wind energy

Intro to wind energy

Exercise 1

Map wind turbines in Rogaland from OSM and calculate the total energy capacity installed.

Objetives

OSM (and how get data from it)
Map points data (interactive map)
Manipulate spatial data (e.g., intersection points - polygons)
Types of data in a data frame
Data wrangling

Solution

Load libraries

Show the code

library(osmdata)
library(sf)
library(tmap)
library(tidyverse)

Get data from OpenStreetMap with osmdata, and transform to a sf object.

Show the code

query <- opq(bbox = "Rogaland", timeout = 50) |> 
   add_osm_feature(key = "generator:source", value = "wind")
data <-  osmdata_sf(query)

wind_turbines <- data$osm_points

Names of the data frame,

Show the code

wind_turbines |>
  names()

 [1] "osm_id"                           "name"                            
 [3] "aeroway:light"                    "aeroway:light:character"         
 [5] "aeroway:light:colour"             "aeroway:light:icao_type"         
 [7] "aeroway:light:intensity"          "brand"                           
 [9] "date_constructed"                 "description"                     
[11] "ele"                              "generator:method"                
[13] "generator:output:electricity"     "generator:source"                
[15] "generator:type"                   "height"                          
[17] "man_made"                         "model"                           
[19] "operator"                         "power"                           
[21] "ref"                              "ref:hinder"                      
[23] "ref:kystverket"                   "seamark:landmark:category"       
[25] "seamark:landmark:conspicuity"     "seamark:landmark:vertical_length"
[27] "seamark:light:1:character"        "seamark:light:1:colour"          
[29] "seamark:light:1:group"            "seamark:light:1:height"          
[31] "seamark:light:1:multiple"         "seamark:light:1:period"          
[33] "seamark:light:1:range"            "seamark:light:2:category"        
[35] "seamark:light:2:character"        "seamark:light:2:colour"          
[37] "seamark:name"                     "seamark:type"                    
[39] "serial_number"                    "source"                          
[41] "geometry"

Clean names

Show the code

wind_turbines <- wind_turbines |>
  janitor::clean_names()

names(wind_turbines)

 [1] "osm_id"                           "name"                            
 [3] "aeroway_light"                    "aeroway_light_character"         
 [5] "aeroway_light_colour"             "aeroway_light_icao_type"         
 [7] "aeroway_light_intensity"          "brand"                           
 [9] "date_constructed"                 "description"                     
[11] "ele"                              "generator_method"                
[13] "generator_output_electricity"     "generator_source"                
[15] "generator_type"                   "height"                          
[17] "man_made"                         "model"                           
[19] "operator"                         "power"                           
[21] "ref"                              "ref_hinder"                      
[23] "ref_kystverket"                   "seamark_landmark_category"       
[25] "seamark_landmark_conspicuity"     "seamark_landmark_vertical_length"
[27] "seamark_light_1_character"        "seamark_light_1_colour"          
[29] "seamark_light_1_group"            "seamark_light_1_height"          
[31] "seamark_light_1_multiple"         "seamark_light_1_period"          
[33] "seamark_light_1_range"            "seamark_light_2_category"        
[35] "seamark_light_2_character"        "seamark_light_2_colour"          
[37] "seamark_name"                     "seamark_type"                    
[39] "serial_number"                    "source"                          
[41] "geometry"

Interactive map with tmap.

Show the code

tmap_mode("view")

tm_shape(wind_turbines) +
  tm_dots(col = "#0072B2")

Figure 1: Wind turbines in Rogaland (Data from OSM)

There are some wind turbines that ar not in Rogaland, so we are going to delect them from the dataset. For that we need to intersect our points (wind turbines) with the polygon (Rogaland).

Show the code

# Get Norwegian counties (polygons)) from GISCO
counties <- giscoR::gisco_get_nuts(country = "NO",
                                   year = "2021",
                                   nuts_level = 3,
                                   epsg = "4326",
                                   resolution = "01") 
# Get only rogaland county
rogaland <- counties |> 
  filter(NUTS_NAME == "Rogaland")

# Intersect wind turbines (points) with rogaland (polygons)
wind_turbines <- wind_turbines |> 
  st_intersection(rogaland)

# Plot
tm_shape(wind_turbines) +
  tm_dots(col = "#0072B2")

Figure 2: Wind turbines in Rogaland (Data from OSM)

Now, we are going to calculate the maximum capacity (MW) installed in the region. If we inspect the data, there is a column describing called generator:output:electricity, which give the information we are looking for in MW. However, if we inspect the columns we see that the values are characters (<chr>) not numbers, so we can not carried out numerical operations on them.

Show the code

glimpse(wind_turbines)

Rows: 256
Columns: 50
$ osm_id                           <chr> "1277731149", "1510248163", "15102482…
$ name                             <chr> "Høg-Jæren energipark 31", "Høg-Jæren…
$ aeroway_light                    <chr> "obstacle", NA, "obstacle", "obstacle…
$ aeroway_light_character          <chr> "flashing", NA, "flashing", "flashing…
$ aeroway_light_colour             <chr> "red", NA, "red", "red", "red", "red"…
$ aeroway_light_icao_type          <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ aeroway_light_intensity          <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ brand                            <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ date_constructed                 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ description                      <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ele                              <chr> "264", "268", "250", "234", "205", "2…
$ generator_method                 <chr> "wind_turbine", "wind_turbine", "wind…
$ generator_output_electricity     <chr> "2.3 MW", "2.3 MW", "2.3 MW", "2.3 MW…
$ generator_source                 <chr> "wind", "wind", "wind", "wind", "wind…
$ generator_type                   <chr> "horizontal_axis", "horizontal_axis",…
$ height                           <chr> "126", "126", "126", "126", "89", "89…
$ man_made                         <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ model                            <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ operator                         <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ power                            <chr> "generator", "generator", "generator"…
$ ref                              <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ref_hinder                       <chr> "70647", "70646", "70640", "71472", "…
$ ref_kystverket                   <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ seamark_landmark_category        <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ seamark_landmark_conspicuity     <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ seamark_landmark_vertical_length <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ seamark_light_1_character        <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ seamark_light_1_colour           <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ seamark_light_1_group            <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ seamark_light_1_height           <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ seamark_light_1_multiple         <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ seamark_light_1_period           <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ seamark_light_1_range            <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ seamark_light_2_category         <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ seamark_light_2_character        <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ seamark_light_2_colour           <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ seamark_name                     <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ seamark_type                     <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ serial_number                    <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ source                           <chr> "Kartverket Luftfartshindre", "Kartve…
$ NUTS_ID                          <chr> "NO0A1", "NO0A1", "NO0A1", "NO0A1", "…
$ LEVL_CODE                        <dbl> 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3…
$ URBN_TYPE                        <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ CNTR_CODE                        <chr> "NO", "NO", "NO", "NO", "NO", "NO", "…
$ NAME_LATN                        <chr> "Rogaland", "Rogaland", "Rogaland", "…
$ NUTS_NAME                        <chr> "Rogaland", "Rogaland", "Rogaland", "…
$ MOUNT_TYPE                       <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ COAST_TYPE                       <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ geo                              <chr> "NO0A1", "NO0A1", "NO0A1", "NO0A1", "…
$ geometry                         <POINT [°]> POINT (5.792002 58.65093), POIN…

Therefore, we need to transform the data to numbers. We can use the function parse_number() from the package readr (inside tidyverse). For not deleting the column, we can generate a new one (e.g., gen_electricity_mw)

Show the code

wind_turbines <- wind_turbines |> 
  mutate(gen_electricity_mw = parse_number(generator_output_electricity) )
     
# Show first 10 values of the column
wind_turbines$gen_electricity_mw |> 
  head(10)

 [1] 2.3 2.3 2.3 2.3  NA  NA 2.3 2.3 2.3 2.3

Remove NA in energy generator:

Show the code

wind_turbines <- wind_turbines |> 
  drop_na(gen_electricity_mw)

# Summary of energy generator (without NA) 
wind_turbines$gen_electricity_mw |> 
  head(10)

 [1] 2.3 2.3 2.3 2.3 2.3 2.3 2.3 2.3 2.3 2.3

calculate the total generation energy capacity.

Show the code

total_capacity_mw <- wind_turbines$gen_electricity_mw |> sum()

print(total_capacity_mw)

[1] 905.3

So the total wind capacity installed in Rogaland is 905.3 MW. Note, that this is only the maximum energy installed and not how much it is generate in the region. It does not take into account the efficiency of the turbines nor wind availability!!.

Exercise 2

Map wind farms and wind turbines from NVE (www.nve.no). The data are free but need to be downloaded from https://nedlasting.nve.no/gis/ before reading into R (save it in a folder: e.g., ~/data/big_data/NVE/NVEData). I have downloaded them in .geojson format. Therefore, we need to read them with the geojsonsf package, which converts GeoJSON to sf objects.

Objetives

Load data from local files
Data wrangling (preprocessing)
Formats of spatial data
Spatial intersections (i.e., points - polygons)
Types of vector data (i.e., lines, points, polygons)
Plot more that one layer in a interactive map

Solution

Show the code

# Libraries
library(geojsonsf)
library(sf)
library(tidyverse)
library(tmap)

Load data from a local file.

Wind turbines (point data)

Show the code

wind_turbines_nve_path <- "data/big_data/NVE/NVEData/Vindkraft_Vindturbin.geojson"
wind_turbines_nve <- geojson_sf(wind_turbines_nve_path) |> 
  # dataUttaksdato to date format
  mutate(dataUttaksdato = ymd(dataUttaksdato))

wind_turbines_nve

Simple feature collection with 272 features and 5 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: 4.905257 ymin: 58.31454 xmax: 6.51592 ymax: 59.41634
Geodetic CRS:  WGS 84
First 10 features:
   objektType   saksTittel saksKategori dataUttaksdato              eksportType
1  Vindturbin      Svåheia            2     2024-02-07 NVEs nedlastningsløsning
2  Vindturbin       Tysvær            2     2024-02-07 NVEs nedlastningsløsning
3  Vindturbin    Høg-Jæren            2     2024-02-07 NVEs nedlastningsløsning
4  Vindturbin       Tysvær            2     2024-02-07 NVEs nedlastningsløsning
5  Vindturbin    Bjerkreim            2     2024-02-07 NVEs nedlastningsløsning
6  Vindturbin Vardafjellet            2     2024-02-07 NVEs nedlastningsløsning
7  Vindturbin    Bjerkreim            2     2024-02-07 NVEs nedlastningsløsning
8  Vindturbin   Måkaknuten            2     2024-02-07 NVEs nedlastningsløsning
9  Vindturbin       Utsira            2     2024-02-07 NVEs nedlastningsløsning
10 Vindturbin     Tellenes            2     2024-02-07 NVEs nedlastningsløsning
                    geometry
1  POINT (6.091838 58.38779)
2  POINT (5.552303 59.31521)
3   POINT (5.76918 58.64514)
4   POINT (5.557336 59.2931)
5  POINT (5.934883 58.59081)
6  POINT (5.894319 58.83959)
7  POINT (5.945595 58.58838)
8  POINT (5.966087 58.68458)
9  POINT (4.906018 59.31497)
10 POINT (6.514851 58.34872)

Wind farms areas (Polygons)

Show the code

wind_farms_nve_path <- "data/big_data/NVE/NVEData/Vindkraft_VindkraftanleggOmr.geojson"
wind_farms_nve <- geojson_sf(wind_farms_nve_path) |> 
  # Coherce to dates format
  mutate(across(.cols = ends_with("dato"), .fns = ymd)) |> 
  # Add ID column
   rowid_to_column("id_farm")

wind_farms_nve

Simple feature collection with 57 features and 15 fields
Geometry type: GEOMETRY
Dimension:     XY
Bounding box:  xmin: 4.890143 ymin: 58.2376 xmax: 6.520988 ymax: 59.61306
Geodetic CRS:  WGS 84
First 10 features:
   id_farm dataUttaksdato totaltAntTurbiner    objektType utAvDriftDato
1        1     2024-02-07              <NA> Vindkraftverk          <NA>
2        2     2024-02-07              <NA> Vindkraftverk          <NA>
3        3     2024-02-07              <NA> Vindkraftverk          <NA>
4        4     2024-02-07              <NA> Vindkraftverk          <NA>
5        5     2024-02-07                37 Vindkraftverk          <NA>
6        6     2024-02-07              <NA> Vindkraftverk          <NA>
7        7     2024-02-07              <NA> Vindkraftverk          <NA>
8        8     2024-02-07              <NA> Vindkraftverk          <NA>
9        9     2024-02-07              <NA> Vindkraftverk          <NA>
10      10     2024-02-07              <NA> Vindkraftverk          <NA>
   idriftDato status fylkesnavn
1        <NA>      V   Rogaland
2        <NA>      V   Rogaland
3        <NA>      V   Rogaland
4        <NA>     FJ   Rogaland
5  2019-12-19      D   Rogaland
6        <NA>     FJ   Rogaland
7        <NA>     FJ   Rogaland
8        <NA>      V   Rogaland
9        <NA>      V   Rogaland
10       <NA>     FJ   Rogaland
                                                                    saksLenke
1  https://www.nve.no/konsesjon/konsesjonssaker/konsesjonssak?type=A-6&id=194
2   https://www.nve.no/konsesjon/konsesjonssaker/konsesjonssak?type=A-6&id=29
3   https://www.nve.no/konsesjon/konsesjonssaker/konsesjonssak?type=A-6&id=36
4   https://www.nve.no/konsesjon/konsesjonssaker/konsesjonssak?type=A-6&id=52
5   https://www.nve.no/konsesjon/konsesjonssaker/konsesjonssak?type=A-6&id=32
6   https://www.nve.no/konsesjon/konsesjonssaker/konsesjonssak?type=A-6&id=37
7   https://www.nve.no/konsesjon/konsesjonssaker/konsesjonssak?type=A-6&id=64
8   https://www.nve.no/konsesjon/konsesjonssaker/konsesjonssak?type=A-6&id=55
9   https://www.nve.no/konsesjon/konsesjonssaker/konsesjonssak?type=A-6&id=53
10 https://www.nve.no/konsesjon/konsesjonssaker/konsesjonssak?type=A-6&id=120
   forventetProduksjon_Gwh kommunenavn                       geometry
1                     34.0   Stavanger POLYGON ((5.616381 59.07174...
2                    408.0   Bjerkreim POLYGON ((5.892451 58.63726...
3                    785.4   Bjerkreim POLYGON ((5.893369 58.70034...
4                    272.0        Lund POLYGON ((6.26167 58.56276,...
5                    558.4   Bjerkreim POLYGON ((5.995785 58.58672...
6                    306.0   Eigersund POLYGON ((6.206857 58.56671...
7                    306.0     Sokndal POLYGON ((6.153781 58.36661...
8                    544.0     Gjesdal POLYGON ((6.293888 58.82249...
9                    265.2   Bjerkreim POLYGON ((6.07609 58.68459,...
10                   204.0      Karmøy POLYGON ((5.29149 59.23401,...
   saksKategori               tiltakshaver effekt_MW              eksportType
1             2 MARIN ENERGI TESTSENTER AS      10.0 NVEs nedlastningsløsning
2             3         LYSE PRODUKSJON AS     120.0 NVEs nedlastningsløsning
3             3         LYSE PRODUKSJON AS     231.0 NVEs nedlastningsløsning
4             4          BJERKREIM VIND AS      80.0 NVEs nedlastningsløsning
5             2          BJERKREIM VIND AS     159.1 NVEs nedlastningsløsning
6             4          BJERKREIM VIND AS      90.0 NVEs nedlastningsløsning
7             4                  ZEPHYR AS      90.0 NVEs nedlastningsløsning
8             2     GILJA VINDKRAFTVERK AS     160.0 NVEs nedlastningsløsning
9             3                  ZEPHYR AS      78.0 NVEs nedlastningsløsning
10            4      EQUINOR WIND POWER AS      60.0 NVEs nedlastningsløsning

Map both datasets together:

Show the code

tmap_mode("view")


tm_shape(wind_farms_nve) + 
  tm_fill("status", alpha = 0.5, title = "Wind farm status") +
  # Add wind turbines
  tm_shape(wind_turbines_nve) +
  tm_dots(col = "#0072B2")

Figure 3: Wind turbines in Rogaland (Data from NVE)

Note: Status

D - Drift (Operations) N - Nedlagt (Decommissioned) O - Ombygd (Rebuilt) P - Planlagt (Planned) P1 - Planlagt illustrert (Planed illustrated) P2 - Planlagt, prosjektert (Planed, projected) U - Under arbeid (in progress) V - Vedtatt (Adopted) FJ - Fjernet (Removed)

To calculate the actual capacity installed, we need to select only the wind farms that are in operation from wind_farms_nve, and sum the power capacity (effekt_MW). We can do that by montds to see the temporal evolution.

Show the code

power_year <- wind_farms_nve |>
  # Get only farm in operation
  filter(status == "D") |> 
  # Summarize power by month
  group_by(year = lubridate::floor_date(idriftDato, "year")) %>%
  summarize(power_MW = sum(effekt_MW)) |> 
  ungroup() |> 
  # Cummulative sum
  mutate(cumsum_power_MW = cumsum(power_MW))

# Column plot
ggplot(data = power_year,
       aes(x = year, y = cumsum_power_MW)) +
  geom_col(fill = "darkblue") +
  labs(title = "Cumulative wind power installed in Rogaland",
       y = "Power [MW]",
       x = "") +
  theme_bw()

We can count now the number of wind turbines per wind farm, to understand for example the volume of blades we may need to recycler.

Show the code

 # Intersect points (wind turbines) wit polygons (wind farms)
number_turbines_farm <- wind_turbines_nve |> 
  # Detect wind farm 
  st_intersection(wind_farms_nve) |> 
  # Number of turbines 
  group_by(id_farm, status,  idriftDato, effekt_MW) |> 
  summarize(n = n()) |> 
  ungroup()

number_turbines_farm

Simple feature collection with 28 features and 5 fields
Geometry type: GEOMETRY
Dimension:     XY
Bounding box:  xmin: 4.905257 ymin: 58.31454 xmax: 6.51592 ymax: 59.41634
Geodetic CRS:  WGS 84
# A tibble: 28 × 6
   id_farm status idriftDato effekt_MW     n                            geometry
     <int> <chr>  <date>         <dbl> <int>                      <GEOMETRY [°]>
 1       2 V      NA              120      1           POINT (5.885113 58.58739)
 2       3 V      NA              231     22 MULTIPOINT ((5.909491 58.66277), (…
 3       5 D      2019-12-19      159.    37 MULTIPOINT ((5.902099 58.58625), (…
 4      14 D      2019-07-25       90     18 MULTIPOINT ((5.871099 58.57491), (…
 5      16 D      2020-06-22       30      7 MULTIPOINT ((5.955698 58.66794), (…
 6      19 D      2020-08-17       30      7 MULTIPOINT ((5.88813 58.8301), (5.…
 7      29 D      2018-02-13       10      3 MULTIPOINT ((5.93595 58.72669), (5…
 8      30 D      2018-10-12       10      2 MULTIPOINT ((5.908438 58.74937), (…
 9      32 P2     NA               15      4 MULTIPOINT ((4.905952 59.31499), (…
10      33 D      2017-11-16      110     33 MULTIPOINT ((6.119956 58.4561), (6…
# ℹ 18 more rows

Now we are going to plot the evolution but in the number od witd turbines in operation:

Show the code

turbines_year <- number_turbines_farm |>
  # Get only farm in operation
  filter(status == "D") |> 
  # Summarize power by month
  group_by(year = lubridate::floor_date(idriftDato, "year")) %>%
  summarize(num_turbines = sum(n)) |> 
  ungroup() |> 
  # Cummulative sum
  mutate(cumsum_num_turbines = cumsum(num_turbines))

# Column plot
ggplot(data = turbines_year,
       aes(x = year, y = cumsum_num_turbines)) +
  geom_col(fill = "darkblue") +
  labs(title = "Cumulative number of wind turbines installed in Rogaland",
       y = "",
       x = "") +
  theme_bw()

How would you improve these figures? For example, we can change the background colour, add subtitles and captions, change font sizes, etc. Can you generate your own figure? Why do you think it looks better? As an example I have created this one, what do you think?

Show the code

caption_text <- "Data source: The Norwegian Water Resources and Energy Directorate (NVE)\nAuthor: Javier Elío (@Elio_javi) - Western Norway University of Applied Sciences"

# Column plot
ggplot(data = power_year,
       aes(x = year, y = cumsum_power_MW)) +
  geom_col(fill = "#0072B2") +
  labs(title = "Wind energy in Rogaland (Norway)",
       subtitle = "Cumulative power installed capacity in MW",
       caption = caption_text,
       y = "",
       x = "") +
  expand_limits(y = c(0, 1700)) +
  theme_bw() +
  theme(
    # Title and captions
    plot.title = element_text(size = 15, colour = "darkblue", face = "bold"),
    plot.subtitle = element_text(size = 10, colour = "grey25"),
    plot.caption = element_text(size = 10, colour = "grey25"),
    # Background colour
    plot.background = element_rect(fill = "linen", colour = NA),
    panel.background = element_rect(fill = "grey85", colour = NA)
  ) +
  # Add arrow
  annotate(
    'curve',
    x = as.Date("2016-01-01"), # Play around with the coordinates until you're satisfied
    y = 800,
    yend = 1600,
    xend = as.Date("2021-01-01"),
    linewidth = 1.5,
    curvature = 0.3,
    col = "#D55E00",
    arrow = arrow(length = unit(0.5, 'cm'))
  ) +
  # Add text
  annotate(
    'text',
    x = as.Date("2012-06-01"),
    y = 1300,
    label = "The installed capacity has nearly\ndoubled between 2017 and 2021.\nWhat will the limit be?",
    size = 3.5,
    hjust = 0
  )

Exercise 3

Objetives

Get wind data from NORA3 link.

The R-script is based on matlab fucntions link

Solution

Show the code

library(terra)
library(tidyterra)
library(eurostat)
library(sf)
library(tidyverse)
library(purrr)
library(patchwork)
library(ncdf4)

Show the code

# # EU map
# box <- st_bbox(c(xmin = -20, xmax = 20, ymax = 45, ymin = 80),
#                 crs = st_crs(4326)) |> 
#   st_as_sfc() |> 
#   st_transform(3035)

eu_countries <- get_eurostat_geospatial(resolution = 10, 
                                        nuts_level = 0, 
                                        year = 2016,
                                        crs = "3035")

Read data directly from the web (wiothout dowloading the data to a local folder

Show the code

#' Get the data of wind speed and direction at specific heihts from a raster
#' @param .r NORA3 data (SpatRaster)
#' @param .height Height to get the data (20, 50, 100, 250, 750)

get_wind_height <- function(.r, .height = 100){
  
  # Velocity vector (y, y)
  ux = subset(.r, paste0("x_wind_", .height, "m"))
  uy = subset(.r, paste0("y_wind_", .height, "m"))
  
  # Calculate magnityd (mag) and direction (dir)
  u_mag = sqrt(ux^2 + uy^2)
  names(u_mag) <- "magnitude"
  u_dir = terra::atan2(y = uy, x = ux) * 180/pi
  names(u_dir) <- "direction"

  # Generate raster 
  u = c(u_mag, u_dir) 
  
  # Output as one raster
  return(u)
  
}

# Function for dowloading wind data from NORA3 
get_wind_z <- function(.year,
                       .month,
                       .day,
                       .hour_group,
                       .lead_time){
  
  # URL of the data
  nora3_url <- paste0("https://thredds.met.no/thredds/dodsC/nora3/",
                      .year,
                      "/",
                      .month,
                      "/",
                      .day, 
                      "/",
                      .hour_group,
                      "/fc", 
                      .year,
                      .month, 
                      .day,
                      .hour_group,
                      "_",
                      .lead_time,
                      "_fp.nc")
  
  # Open the netCDF file
  ncin <- ncdf4::nc_open(nora3_url)
  
  # Get coordinate  variables
  lon <- ncdf4::ncvar_get(ncin,"x")
  lat <- ncdf4::ncvar_get(ncin,"y")
  
  # Get time
  time <- ncdf4::ncvar_get(ncin,"time")
  
  # Get wind speed at 10 m above ground (height4)
  dname <- "wind_speed"
  ws10_array <- ncdf4::ncvar_get(ncin,dname)
  dlname <- ncdf4::ncatt_get(ncin,dname,"standard_name")
  dunits <- ncdf4::ncatt_get(ncin,dname,"units")
  fillvalue <- ncdf4::ncatt_get(ncin,dname,"_FillValue")
  # replace netCDF fill values with NA's
  ws10_array[ws10_array == fillvalue$value] <- NA
  
  # Get wind direction at 10 m above ground (height4)
  dname <- "wind_direction"
  wd10_array <- ncdf4::ncvar_get(ncin,dname)
  dlname <- ncdf4::ncatt_get(ncin,dname,"standard_name")
  dunits <- ncdf4::ncatt_get(ncin,dname,"units")
  fillvalue <- ncdf4::ncatt_get(ncin,dname,"_FillValue")
  # replace netCDF fill values with NA's
  wd10_array[wd10_array == fillvalue$value] <- NA
  
  # x_wind_z[x,y,height2,time] 
  # ncdf4::ncvar_get(ncin,"height2") -- 20  50 100 250 500 750 m above ground
  dname <- "x_wind_z"
  xh2_array <- ncdf4::ncvar_get(ncin,dname)
  dlname <- ncdf4::ncatt_get(ncin,dname,"standard_name")
  dunits <- ncdf4::ncatt_get(ncin,dname,"units")
  fillvalue <- ncdf4::ncatt_get(ncin,dname,"_FillValue")
  # replace netCDF fill values with NA's
  xh2_array[xh2_array == fillvalue$value] <- NA
  
  # y_wind_z[x,y,height2,time]
  dname <- "y_wind_z"
  yh2_array <- ncdf4::ncvar_get(ncin,dname)
  dlname <- ncdf4::ncatt_get(ncin,dname,"standard_name")
  dunits <- ncdf4::ncatt_get(ncin,dname,"units")
  fillvalue <- ncdf4::ncatt_get(ncin,dname,"_FillValue")
  # replace netCDF fill values with NA's
  yh2_array[yh2_array == fillvalue$value] <- NA
  
  
  # create dataframe with values
  df <- expand.grid(lon,lat) |> 
    as_tibble() |> 
    dplyr::rename_with(~ c("lon", "lat"), 1:2) |> 
    # Add wind speed and direction at 10 m
    dplyr::mutate(wind10_mag = as.vector(ws10_array),
                  wind10_dir = as.vector(wd10_array)) |> 
    # Add wind speed at h2 
    dplyr::mutate(
      # x_wind_z
      x_wind_20m  = as.vector(xh2_array[ , , 1]),
      x_wind_50m  = as.vector(xh2_array[ , , 2]),
      x_wind_100m = as.vector(xh2_array[ , , 3]),
      x_wind_250m = as.vector(xh2_array[ , , 4]),
      x_wind_500m = as.vector(xh2_array[ , , 5]),
      x_wind_750m = as.vector(xh2_array[ , , 6]),
      # y_wind_z 
      y_wind_20m  = as.vector(yh2_array[ , , 1]),
      y_wind_50m  = as.vector(yh2_array[ , , 2]),
      y_wind_100m = as.vector(yh2_array[ , , 3]),
      y_wind_250m = as.vector(yh2_array[ , , 4]),
      y_wind_500m = as.vector(yh2_array[ , , 5]),
      y_wind_750m = as.vector(yh2_array[ , , 6])
    )
  
  # Create Raster with all data
  r_crs <- "+proj=lcc +lat_0=66.3 +lon_0=-42 +lat_1=66.3 +lat_2=66.3 +x_0=0 +y_0=0 +R=6371000 +units=m +no_defs"
  r <- tidyterra::as_spatraster(df, crs = r_crs, digits = 4)
  # Add time 
  time(r) <- rep(lubridate::as_datetime(time, tz = "UTC"), times = length(names(r)))
  
  # get wind speed and direction at all heights
  ff <- function(.height) { get_wind_height(r, .height) }
  u_height_list <- c(20, 50, 100, 250, 750)
  u_height <- map(u_height_list, ff)
  names(u_height) <- c("wind20", "wind50", "wind100", "wind250", "wind750")
  u_height <- u_height |>
    rast() |> 
    rename_with( ~ gsub("_1", "_mag", .x, fixed = TRUE)) |> 
    rename_with( ~ gsub("_2", "_dir", .x, fixed = TRUE))
  
  # Retrurn SpatRaster object
  rr <- c(tidyterra::select(r, wind10_mag,  wind10_dir), u_height)
  
  return(rr)
  
}

Show the code

wind_nora3 <- get_wind_z(.year = "2018",
                         .month = "11",
                         .day = "08",
                         .hour_group = "00",
                         .lead_time = "004")

wind_nora3

class       : SpatRaster 
dimensions  : 1489, 889, 12  (nrow, ncol, nlyr)
resolution  : 2999.875, 2999.969  (x, y)
extent      : 776860.9, 3443750, -1271977, 3194976  (xmin, xmax, ymin, ymax)
coord. ref. : +proj=lcc +lat_0=66.3 +lon_0=-42 +lat_1=66.3 +lat_2=66.3 +x_0=0 +y_0=0 +R=6371000 +units=m +no_defs 
source(s)   : memory
names       :   wind10_mag,   wind10_dir,   wind20_mag, wind20_dir,   wind50_mag, wind50_dir, ... 
min values  :  0.004474435, 3.791680e-04,  0.003715257,  -179.9983,  0.006306755,  -179.9968, ... 
max values  : 26.079982758, 3.599998e+02, 27.383629015,   179.9978, 29.652804073,   179.9996, ... 
time        : 2018-11-08 04:00:00 UTC

Show the code

ggplot() +
  geom_spatraster(data =  select(wind_nora3 , ends_with("mag"))) +
  facet_wrap(~lyr, ncol = 2) +
  scale_fill_whitebox_c(name = "u [m/s]", palette = "viridi") +
  geom_sf(data = eu_countries, fill = NA, colour = "white") +
  coord_sf(expand = FALSE, crs = sf::st_crs(3035)) +
  scale_x_continuous(limits = c(3000000, 5000000)) +
  scale_y_continuous(limits = c(3600000, 5300000)) +
  labs(title = terra::time(wind_nora3)[1]) +
  theme_bw()

Extract values from one point (e.g., near Utsira - Coordinates from google maps [WGS84]: 59.346588 (lat), 4.899842 (long)).

Show the code

# Point 
xy <- cbind(4.899842, 59.346588)
point <- vect(xy, crs="+proj=longlat +datum=WGS84") |> 
  project(crs(wind_nora3))

# Extract values
p_wind <- terra::extract(wind_nora3, point) |>  
  select(ends_with("_mag")) |> 
  pivot_longer(cols = everything()) |> 
  rename(height_m = name,
         wind_m_s = value) |> 
  mutate(height_m = readr::parse_number(height_m))
  

# Plot
ggplot() + 
  geom_point(data = p_wind,
             aes(x = wind_m_s, y = height_m ),
             col = "blue") +
  labs(x = "u [m/s]",
       y = "height [m]",
       title = "Vertical wind profile at point: 59.346588 (lat), 4.899842 (long)", 
       subtitle = terra::time(wind_nora3)[1]) +
  theme_bw()

Interpolation wind profile (Solbrekke and Sorteberg 2022):

Exponential relation:

\[ u_{z_2}(t) = u_{z_1}(t) \Big(\frac{z_2}{z_1}\Big)^{\alpha(t)} \]
Exponential power law coefficient:

\[ \alpha(t) = \frac{ln\Big( \frac{u_{z_2}(t)}{u_{z_1}(t)} \Big)}{ln\Big( \frac{z_2}{z_1} \Big)} \]

Show the code

get_inter_wind_profile <- function(x){
  
  # Vector with alpha depending on height
  alpha <- rep(NA, 5)
  for(i in seq_along(alpha)) {
    alpha[i] = as.numeric( (log(x[i, 2]/x[i+1, 2]) / log(x[i, 1]/x[i+1, 1])) )
  }
  
  # Create data frame for interpolation (steps = 1) 
  wind_inter <- tibble(height_m = seq(10, 750, 1)) |> 
    mutate(wind_m_s = NA) |> 
    mutate(alpha = case_when(
      height_m <= 20 ~ alpha[1],
      height_m >  20 &  height_m <=  50 ~ alpha[2],
      height_m >  50 &  height_m <= 100 ~ alpha[3],
      height_m > 100 &  height_m <= 250 ~ alpha[4],
      height_m > 250 &  height_m <= 750 ~ alpha[5]
    ))  
  
  # Interpolation (based on u [m/s] at 10 m)
  wind_inter$wind_m_s[1] = x[x["height_m"] == 10, ]$wind_m_s
  for(i in 2:length(wind_inter$height_m)) {
    wind_inter$wind_m_s[i] = wind_inter$wind_m_s[i-1] * (wind_inter$height_m[i] / wind_inter$height_m[i-1])^wind_inter$alpha[i]
  }
  
  return(wind_inter)
  
}
  
wind_inter <- get_inter_wind_profile(p_wind)

# Plot
ggplot() + 
  geom_point(data = p_wind,
             aes(x = wind_m_s, y = height_m ),
             col = "blue") +
  geom_line(data = wind_inter,
             aes(x = wind_m_s, y = height_m ),
             col = "red") + 
  labs(x = "u [m/s]",
       y = "height [m]",
       title = "Vertical wind profile at point: 59.346588 (lat), 4.899842 (long)", 
       subtitle = terra::time(wind_nora3)[1]) +
  theme_bw()

Exercise 4

Objetives

Time series wind using NORA3 data

Analyse wind speed and direction over time in a point at 100 m hight.

Solution

Show the code

library(terra)
library(tidyterra)
library(eurostat)
library(sf)
library(tidyverse)
library(lubridate)
library(photobiology)
library(purrr)
library(patchwork)
library(ncdf4)
library(gt)

Read data that have been previously downloaded into a local file.

Show the code

wind_100m <- readRDS("data/wind_100m.rds") 

wind_100m <- wind_100m |> 
  # Add columns indicatind day/night time
  mutate(day_night = ifelse(is_daytime(time, tz = "Europe/Oslo"), "day", "night"))

wind_100m |>
  gt()

year	month	day	hour_group	lead_time	long	lat	ID	u_mag	u_dir	time	day_night
2018	01	01	00	004	4.899842	59.34659	1	13.4393594	105.3728971	2018-01-01 04:00:00	night
2018	01	01	00	005	4.899842	59.34659	1	13.3255420	100.5657463	2018-01-01 05:00:00	night
2018	01	01	00	006	4.899842	59.34659	1	13.5051916	99.1950790	2018-01-01 06:00:00	night
2018	01	01	00	007	4.899842	59.34659	1	11.2424514	81.8105987	2018-01-01 07:00:00	night
2018	01	01	00	008	4.899842	59.34659	1	11.2823779	85.8412149	2018-01-01 08:00:00	night
2018	01	01	00	009	4.899842	59.34659	1	10.7900183	72.5036945	2018-01-01 09:00:00	day
2018	01	01	06	004	4.899842	59.34659	1	7.4036502	13.5743332	2018-01-01 10:00:00	day
2018	01	01	06	005	4.899842	59.34659	1	8.0308436	10.8492618	2018-01-01 11:00:00	day
2018	01	01	06	006	4.899842	59.34659	1	9.0159255	6.9195379	2018-01-01 12:00:00	day
2018	01	01	06	007	4.899842	59.34659	1	7.7525221	13.5502902	2018-01-01 13:00:00	day
2018	01	01	06	008	4.899842	59.34659	1	7.8558757	5.0274267	2018-01-01 14:00:00	day
2018	01	01	06	009	4.899842	59.34659	1	7.1992725	3.2982519	2018-01-01 15:00:00	day
2018	01	01	12	004	4.899842	59.34659	1	9.1077356	-0.2239485	2018-01-01 16:00:00	night
2018	01	01	12	005	4.899842	59.34659	1	9.5103237	2.1884164	2018-01-01 17:00:00	night
2018	01	01	12	006	4.899842	59.34659	1	9.9928377	2.1185059	2018-01-01 18:00:00	night
2018	01	01	12	007	4.899842	59.34659	1	10.1710031	4.2324802	2018-01-01 19:00:00	night
2018	01	01	12	008	4.899842	59.34659	1	9.9837195	2.2040420	2018-01-01 20:00:00	night
2018	01	01	12	009	4.899842	59.34659	1	9.6288718	1.2356494	2018-01-01 21:00:00	night
2018	01	01	18	004	4.899842	59.34659	1	9.6354665	2.7036963	2018-01-01 22:00:00	night
2018	01	01	18	005	4.899842	59.34659	1	9.6891242	3.8853679	2018-01-01 23:00:00	night
2018	01	01	18	006	4.899842	59.34659	1	9.4839699	1.3019254	2018-01-02	night
2018	01	01	18	007	4.899842	59.34659	1	8.7628428	0.3324338	2018-01-02 01:00:00	night
2018	01	01	18	008	4.899842	59.34659	1	8.2817781	3.2307277	2018-01-02 02:00:00	night
2018	01	01	18	009	4.899842	59.34659	1	7.9362150	-0.6433558	2018-01-02 03:00:00	night
2018	01	02	00	004	4.899842	59.34659	1	8.0583002	-1.7832517	2018-01-02 04:00:00	night
2018	01	02	00	005	4.899842	59.34659	1	7.5283060	-1.4084536	2018-01-02 05:00:00	night
2018	01	02	00	006	4.899842	59.34659	1	6.8727441	1.4844225	2018-01-02 06:00:00	night
2018	01	02	00	007	4.899842	59.34659	1	6.3204620	6.4400305	2018-01-02 07:00:00	night
2018	01	02	00	008	4.899842	59.34659	1	5.8913214	9.5355364	2018-01-02 08:00:00	night
2018	01	02	00	009	4.899842	59.34659	1	4.7071529	13.1489744	2018-01-02 09:00:00	day
2018	01	02	06	004	4.899842	59.34659	1	3.7404010	25.0828643	2018-01-02 10:00:00	day
2018	01	02	06	005	4.899842	59.34659	1	3.5810539	41.9025410	2018-01-02 11:00:00	day
2018	01	02	06	006	4.899842	59.34659	1	3.5064393	50.4021880	2018-01-02 12:00:00	day
2018	01	02	06	007	4.899842	59.34659	1	4.2846127	64.4093018	2018-01-02 13:00:00	day
2018	01	02	06	008	4.899842	59.34659	1	2.4497540	86.6091039	2018-01-02 14:00:00	day
2018	01	02	06	009	4.899842	59.34659	1	4.3818628	107.8187668	2018-01-02 15:00:00	day
2018	01	02	12	004	4.899842	59.34659	1	4.7289017	122.5705474	2018-01-02 16:00:00	night
2018	01	02	12	005	4.899842	59.34659	1	5.5337164	127.2560564	2018-01-02 17:00:00	night
2018	01	02	12	006	4.899842	59.34659	1	6.1743297	136.5981852	2018-01-02 18:00:00	night
2018	01	02	12	007	4.899842	59.34659	1	7.3246164	148.0861042	2018-01-02 19:00:00	night
2018	01	02	12	008	4.899842	59.34659	1	8.8395053	151.7909553	2018-01-02 20:00:00	night
2018	01	02	12	009	4.899842	59.34659	1	10.0588595	163.8801560	2018-01-02 21:00:00	night
2018	01	02	18	004	4.899842	59.34659	1	11.7548057	179.8165908	2018-01-02 22:00:00	night
2018	01	02	18	005	4.899842	59.34659	1	11.9327245	-177.1665976	2018-01-02 23:00:00	night
2018	01	02	18	006	4.899842	59.34659	1	12.1709078	-174.0704402	2018-01-03	night
2018	01	02	18	007	4.899842	59.34659	1	13.5692188	-178.1050272	2018-01-03 01:00:00	night
2018	01	02	18	008	4.899842	59.34659	1	12.8353008	-176.3596484	2018-01-03 02:00:00	night
2018	01	02	18	009	4.899842	59.34659	1	12.0062975	-161.5028788	2018-01-03 03:00:00	night
2018	01	03	00	004	4.899842	59.34659	1	11.4380405	-155.3844896	2018-01-03 04:00:00	night
2018	01	03	00	005	4.899842	59.34659	1	10.9728374	-144.6271965	2018-01-03 05:00:00	night
2018	01	03	00	006	4.899842	59.34659	1	9.6806672	-140.9868817	2018-01-03 06:00:00	night
2018	01	03	00	007	4.899842	59.34659	1	8.6439929	-140.5788210	2018-01-03 07:00:00	night
2018	01	03	00	008	4.899842	59.34659	1	7.1499628	-117.1284066	2018-01-03 08:00:00	night
2018	01	03	00	009	4.899842	59.34659	1	7.7838097	-109.6204521	2018-01-03 09:00:00	day
2018	01	03	06	004	4.899842	59.34659	1	8.9428845	-103.7537081	2018-01-03 10:00:00	day
2018	01	03	06	005	4.899842	59.34659	1	9.3042986	-101.4344305	2018-01-03 11:00:00	day
2018	01	03	06	006	4.899842	59.34659	1	9.6583382	-94.4485039	2018-01-03 12:00:00	day
2018	01	03	06	007	4.899842	59.34659	1	9.3765114	-97.5950685	2018-01-03 13:00:00	day
2018	01	03	06	008	4.899842	59.34659	1	7.9441262	-100.7920868	2018-01-03 14:00:00	day
2018	01	03	06	009	4.899842	59.34659	1	7.0033566	-102.4070942	2018-01-03 15:00:00	day
2018	01	03	12	004	4.899842	59.34659	1	9.2016386	-96.5586314	2018-01-03 16:00:00	night
2018	01	03	12	005	4.899842	59.34659	1	7.6875175	-98.8389378	2018-01-03 17:00:00	night
2018	01	03	12	006	4.899842	59.34659	1	6.1868378	-103.3415169	2018-01-03 18:00:00	night
2018	01	03	12	007	4.899842	59.34659	1	4.9667468	-108.0304943	2018-01-03 19:00:00	night
2018	01	03	12	008	4.899842	59.34659	1	5.3295327	-108.0161626	2018-01-03 20:00:00	night
2018	01	03	12	009	4.899842	59.34659	1	5.2799468	-102.1967813	2018-01-03 21:00:00	night
2018	01	03	18	004	4.899842	59.34659	1	7.0256463	-112.5890615	2018-01-03 22:00:00	night
2018	01	03	18	005	4.899842	59.34659	1	6.4315630	-121.3735685	2018-01-03 23:00:00	night
2018	01	03	18	006	4.899842	59.34659	1	6.4885012	-130.8004783	2018-01-04	night
2018	01	03	18	007	4.899842	59.34659	1	4.8255426	-125.0452004	2018-01-04 01:00:00	night
2018	01	03	18	008	4.899842	59.34659	1	3.7325165	-122.6689825	2018-01-04 02:00:00	night
2018	01	03	18	009	4.899842	59.34659	1	2.9611050	-122.3318374	2018-01-04 03:00:00	night
2018	01	04	00	004	4.899842	59.34659	1	6.0586949	-99.1981224	2018-01-04 04:00:00	night
2018	01	04	00	005	4.899842	59.34659	1	5.8850520	-102.5572154	2018-01-04 05:00:00	night
2018	01	04	00	006	4.899842	59.34659	1	4.7108418	-117.3612611	2018-01-04 06:00:00	night
2018	01	04	00	007	4.899842	59.34659	1	4.1915827	-124.4108225	2018-01-04 07:00:00	night
2018	01	04	00	008	4.899842	59.34659	1	4.1119075	-127.1962250	2018-01-04 08:00:00	night
2018	01	04	00	009	4.899842	59.34659	1	4.8670429	-128.6301839	2018-01-04 09:00:00	day
2018	01	04	06	004	4.899842	59.34659	1	5.8102671	-133.2979209	2018-01-04 10:00:00	day
2018	01	04	06	005	4.899842	59.34659	1	4.8776319	-151.8410353	2018-01-04 11:00:00	day
2018	01	04	06	006	4.899842	59.34659	1	4.6805715	-159.9302724	2018-01-04 12:00:00	day
2018	01	04	06	007	4.899842	59.34659	1	5.7089988	-173.2597185	2018-01-04 13:00:00	day
2018	01	04	06	008	4.899842	59.34659	1	6.0838413	-175.0490322	2018-01-04 14:00:00	day
2018	01	04	06	009	4.899842	59.34659	1	5.6045673	-168.4802642	2018-01-04 15:00:00	day
2018	01	04	12	004	4.899842	59.34659	1	5.7287654	-155.4345061	2018-01-04 16:00:00	night
2018	01	04	12	005	4.899842	59.34659	1	5.1201096	-155.4478123	2018-01-04 17:00:00	night
2018	01	04	12	006	4.899842	59.34659	1	4.8979129	-143.8133348	2018-01-04 18:00:00	night
2018	01	04	12	007	4.899842	59.34659	1	5.0777394	-132.4595309	2018-01-04 19:00:00	night
2018	01	04	12	008	4.899842	59.34659	1	4.4910341	-136.9277618	2018-01-04 20:00:00	night
2018	01	04	12	009	4.899842	59.34659	1	3.4905239	-130.7218893	2018-01-04 21:00:00	night
2018	01	04	18	004	4.899842	59.34659	1	4.5447734	-112.5000350	2018-01-04 22:00:00	night
2018	01	04	18	005	4.899842	59.34659	1	4.2134023	-112.1594423	2018-01-04 23:00:00	night
2018	01	04	18	006	4.899842	59.34659	1	4.6769542	-110.0077171	2018-01-05	night
2018	01	04	18	007	4.899842	59.34659	1	3.6139780	-105.7770235	2018-01-05 01:00:00	night
2018	01	04	18	008	4.899842	59.34659	1	3.7151901	-86.0969984	2018-01-05 02:00:00	night
2018	01	04	18	009	4.899842	59.34659	1	4.4537730	-88.1786419	2018-01-05 03:00:00	night
2018	01	05	00	004	4.899842	59.34659	1	5.5489703	-88.3580593	2018-01-05 04:00:00	night
2018	01	05	00	005	4.899842	59.34659	1	4.4415259	-85.4711447	2018-01-05 05:00:00	night
2018	01	05	00	006	4.899842	59.34659	1	3.3331542	-83.7109613	2018-01-05 06:00:00	night
2018	01	05	00	007	4.899842	59.34659	1	3.1458131	-97.3173301	2018-01-05 07:00:00	night
2018	01	05	00	008	4.899842	59.34659	1	2.4643512	-104.4196540	2018-01-05 08:00:00	night
2018	01	05	00	009	4.899842	59.34659	1	2.7836981	-108.4544148	2018-01-05 09:00:00	day
2018	01	05	06	004	4.899842	59.34659	1	3.7178263	-81.8754846	2018-01-05 10:00:00	day
2018	01	05	06	005	4.899842	59.34659	1	3.1105651	-123.1721421	2018-01-05 11:00:00	day
2018	01	05	06	006	4.899842	59.34659	1	3.3225617	-160.5752324	2018-01-05 12:00:00	day
2018	01	05	06	007	4.899842	59.34659	1	2.7137617	176.3080747	2018-01-05 13:00:00	day
2018	01	05	06	008	4.899842	59.34659	1	2.7918153	171.3827850	2018-01-05 14:00:00	day
2018	01	05	06	009	4.899842	59.34659	1	2.7778055	166.9924086	2018-01-05 15:00:00	day
2018	01	05	12	004	4.899842	59.34659	1	0.7968741	-78.1715192	2018-01-05 16:00:00	night
2018	01	05	12	005	4.899842	59.34659	1	0.8266747	-151.7344133	2018-01-05 17:00:00	night
2018	01	05	12	006	4.899842	59.34659	1	1.2806361	-179.1718807	2018-01-05 18:00:00	night
2018	01	05	12	007	4.899842	59.34659	1	0.4865575	-134.8996261	2018-01-05 19:00:00	night
2018	01	05	12	008	4.899842	59.34659	1	1.4327982	-70.4381171	2018-01-05 20:00:00	night
2018	01	05	12	009	4.899842	59.34659	1	2.1203856	-64.2770989	2018-01-05 21:00:00	night
2018	01	05	18	004	4.899842	59.34659	1	4.5835482	-37.6033878	2018-01-05 22:00:00	night
2018	01	05	18	005	4.899842	59.34659	1	4.9567464	-36.5121994	2018-01-05 23:00:00	night
2018	01	05	18	006	4.899842	59.34659	1	6.9401152	-34.9610600	2018-01-06	night
2018	01	05	18	007	4.899842	59.34659	1	11.7955986	-47.7712222	2018-01-06 01:00:00	night
2018	01	05	18	008	4.899842	59.34659	1	13.3045512	-51.0694266	2018-01-06 02:00:00	night
2018	01	05	18	009	4.899842	59.34659	1	14.1802154	-50.2742132	2018-01-06 03:00:00	night
2018	01	06	00	004	4.899842	59.34659	1	11.8539891	-55.5560091	2018-01-06 04:00:00	night
2018	01	06	00	005	4.899842	59.34659	1	11.8503852	-56.8267369	2018-01-06 05:00:00	night
2018	01	06	00	006	4.899842	59.34659	1	12.3296398	-61.1956274	2018-01-06 06:00:00	night
2018	01	06	00	007	4.899842	59.34659	1	10.9836402	-68.6821347	2018-01-06 07:00:00	night
2018	01	06	00	008	4.899842	59.34659	1	10.0745509	-67.4908056	2018-01-06 08:00:00	night
2018	01	06	00	009	4.899842	59.34659	1	9.8474303	-62.3296784	2018-01-06 09:00:00	day
2018	01	06	06	004	4.899842	59.34659	1	11.1148422	-55.8201657	2018-01-06 10:00:00	day
2018	01	06	06	005	4.899842	59.34659	1	12.1388800	-51.9165448	2018-01-06 11:00:00	day
2018	01	06	06	006	4.899842	59.34659	1	13.3910060	-49.0835808	2018-01-06 12:00:00	day
2018	01	06	06	007	4.899842	59.34659	1	12.6857600	-50.4880195	2018-01-06 13:00:00	day
2018	01	06	06	008	4.899842	59.34659	1	12.3438330	-53.6416428	2018-01-06 14:00:00	day
2018	01	06	06	009	4.899842	59.34659	1	12.4576439	-52.0348933	2018-01-06 15:00:00	day
2018	01	06	12	004	4.899842	59.34659	1	10.0821936	-51.5760198	2018-01-06 16:00:00	day
2018	01	06	12	005	4.899842	59.34659	1	10.1254665	-54.7253073	2018-01-06 17:00:00	night
2018	01	06	12	006	4.899842	59.34659	1	10.8768196	-53.2075231	2018-01-06 18:00:00	night
2018	01	06	12	007	4.899842	59.34659	1	10.7950570	-53.2290367	2018-01-06 19:00:00	night
2018	01	06	12	008	4.899842	59.34659	1	10.5674593	-55.0127038	2018-01-06 20:00:00	night
2018	01	06	12	009	4.899842	59.34659	1	8.8388816	-61.6410825	2018-01-06 21:00:00	night
2018	01	06	18	004	4.899842	59.34659	1	7.5040679	-49.0575608	2018-01-06 22:00:00	night
2018	01	06	18	005	4.899842	59.34659	1	6.2151854	-55.7452003	2018-01-06 23:00:00	night
2018	01	06	18	006	4.899842	59.34659	1	4.2384605	-79.5528356	2018-01-07	night
2018	01	06	18	007	4.899842	59.34659	1	2.2198107	-101.7772373	2018-01-07 01:00:00	night
2018	01	06	18	008	4.899842	59.34659	1	1.6637324	-174.3946618	2018-01-07 02:00:00	night
2018	01	06	18	009	4.899842	59.34659	1	2.0405656	127.1030565	2018-01-07 03:00:00	night
2018	01	07	00	004	4.899842	59.34659	1	4.8087240	50.4702059	2018-01-07 04:00:00	night
2018	01	07	00	005	4.899842	59.34659	1	5.8384787	50.4679636	2018-01-07 05:00:00	night
2018	01	07	00	006	4.899842	59.34659	1	6.5808418	55.9392229	2018-01-07 06:00:00	night
2018	01	07	00	007	4.899842	59.34659	1	7.8513285	59.9253105	2018-01-07 07:00:00	night
2018	01	07	00	008	4.899842	59.34659	1	8.9390804	65.4823071	2018-01-07 08:00:00	night
2018	01	07	00	009	4.899842	59.34659	1	10.9099635	72.5028781	2018-01-07 09:00:00	day
2018	01	07	06	004	4.899842	59.34659	1	12.5303391	64.4007553	2018-01-07 10:00:00	day
2018	01	07	06	005	4.899842	59.34659	1	14.4269911	65.8557966	2018-01-07 11:00:00	day
2018	01	07	06	006	4.899842	59.34659	1	15.7242339	60.0687802	2018-01-07 12:00:00	day
2018	01	07	06	007	4.899842	59.34659	1	16.2590679	53.9684954	2018-01-07 13:00:00	day
2018	01	07	06	008	4.899842	59.34659	1	16.7224407	49.7631781	2018-01-07 14:00:00	day
2018	01	07	06	009	4.899842	59.34659	1	16.1227728	46.4987752	2018-01-07 15:00:00	day
2018	01	07	12	004	4.899842	59.34659	1	16.4044671	44.9287004	2018-01-07 16:00:00	day
2018	01	07	12	005	4.899842	59.34659	1	15.6186615	43.7540981	2018-01-07 17:00:00	night
2018	01	07	12	006	4.899842	59.34659	1	14.4705616	42.2503125	2018-01-07 18:00:00	night
2018	01	07	12	007	4.899842	59.34659	1	14.0194586	41.2838297	2018-01-07 19:00:00	night
2018	01	07	12	008	4.899842	59.34659	1	13.2921381	40.9935583	2018-01-07 20:00:00	night
2018	01	07	12	009	4.899842	59.34659	1	12.2931160	44.0816486	2018-01-07 21:00:00	night
2018	01	07	18	004	4.899842	59.34659	1	11.1508259	35.8653087	2018-01-07 22:00:00	night
2018	01	07	18	005	4.899842	59.34659	1	9.5573464	36.8940106	2018-01-07 23:00:00	night
2018	01	07	18	006	4.899842	59.34659	1	7.9562834	43.1045668	2018-01-08	night
2018	01	07	18	007	4.899842	59.34659	1	7.0790128	54.2111943	2018-01-08 01:00:00	night
2018	01	07	18	008	4.899842	59.34659	1	6.8346431	62.6467392	2018-01-08 02:00:00	night
2018	01	07	18	009	4.899842	59.34659	1	6.4543540	70.2285909	2018-01-08 03:00:00	night
2018	01	08	00	004	4.899842	59.34659	1	6.9366103	91.7568422	2018-01-08 04:00:00	night
2018	01	08	00	005	4.899842	59.34659	1	6.7032924	91.4971668	2018-01-08 05:00:00	night
2018	01	08	00	006	4.899842	59.34659	1	6.2855655	92.7599329	2018-01-08 06:00:00	night
2018	01	08	00	007	4.899842	59.34659	1	5.6388049	96.6331808	2018-01-08 07:00:00	night
2018	01	08	00	008	4.899842	59.34659	1	5.4803240	102.3457739	2018-01-08 08:00:00	night
2018	01	08	00	009	4.899842	59.34659	1	5.9506504	106.0683985	2018-01-08 09:00:00	day
2018	01	08	06	004	4.899842	59.34659	1	5.8774331	109.0361140	2018-01-08 10:00:00	day
2018	01	08	06	005	4.899842	59.34659	1	5.8040716	120.2039159	2018-01-08 11:00:00	day
2018	01	08	06	006	4.899842	59.34659	1	6.5855676	127.8896659	2018-01-08 12:00:00	day
2018	01	08	06	007	4.899842	59.34659	1	6.6265308	130.1749599	2018-01-08 13:00:00	day
2018	01	08	06	008	4.899842	59.34659	1	6.2167132	130.8595390	2018-01-08 14:00:00	day
2018	01	08	06	009	4.899842	59.34659	1	5.6648564	137.1267510	2018-01-08 15:00:00	day
2018	01	08	12	004	4.899842	59.34659	1	5.8317201	150.3017542	2018-01-08 16:00:00	day
2018	01	08	12	005	4.899842	59.34659	1	6.7354917	158.2936535	2018-01-08 17:00:00	night
2018	01	08	12	006	4.899842	59.34659	1	8.5859532	155.2638177	2018-01-08 18:00:00	night
2018	01	08	12	007	4.899842	59.34659	1	9.2544399	148.8194193	2018-01-08 19:00:00	night
2018	01	08	12	008	4.899842	59.34659	1	9.6886700	151.9108566	2018-01-08 20:00:00	night
2018	01	08	12	009	4.899842	59.34659	1	10.1714799	162.6006329	2018-01-08 21:00:00	night
2018	01	08	18	004	4.899842	59.34659	1	10.6025254	161.9337098	2018-01-08 22:00:00	night
2018	01	08	18	005	4.899842	59.34659	1	10.7549572	162.8144604	2018-01-08 23:00:00	night
2018	01	08	18	006	4.899842	59.34659	1	11.8828517	160.3873821	2018-01-09	night
2018	01	08	18	007	4.899842	59.34659	1	11.8221177	159.9648512	2018-01-09 01:00:00	night
2018	01	08	18	008	4.899842	59.34659	1	12.4005348	163.4464628	2018-01-09 02:00:00	night
2018	01	08	18	009	4.899842	59.34659	1	12.8081524	166.9986554	2018-01-09 03:00:00	night
2018	01	09	00	004	4.899842	59.34659	1	13.0545386	172.9288355	2018-01-09 04:00:00	night
2018	01	09	00	005	4.899842	59.34659	1	13.2287273	173.6410883	2018-01-09 05:00:00	night
2018	01	09	00	006	4.899842	59.34659	1	13.4845059	176.4512072	2018-01-09 06:00:00	night
2018	01	09	00	007	4.899842	59.34659	1	13.4685260	-178.9011732	2018-01-09 07:00:00	night
2018	01	09	00	008	4.899842	59.34659	1	13.9169409	-176.9931788	2018-01-09 08:00:00	night
2018	01	09	00	009	4.899842	59.34659	1	14.3269205	-177.5574457	2018-01-09 09:00:00	day
2018	01	09	06	004	4.899842	59.34659	1	14.3451135	-177.5104021	2018-01-09 10:00:00	day
2018	01	09	06	005	4.899842	59.34659	1	13.8986666	-179.0356555	2018-01-09 11:00:00	day
2018	01	09	06	006	4.899842	59.34659	1	13.3991934	179.8374029	2018-01-09 12:00:00	day
2018	01	09	06	007	4.899842	59.34659	1	13.7868714	-179.5073398	2018-01-09 13:00:00	day
2018	01	09	06	008	4.899842	59.34659	1	14.2526489	-177.6876339	2018-01-09 14:00:00	day
2018	01	09	06	009	4.899842	59.34659	1	15.2314251	-175.9015668	2018-01-09 15:00:00	day
2018	01	09	12	004	4.899842	59.34659	1	16.1458319	-168.6977984	2018-01-09 16:00:00	day
2018	01	09	12	005	4.899842	59.34659	1	16.2047403	-168.9802124	2018-01-09 17:00:00	night
2018	01	09	12	006	4.899842	59.34659	1	16.2250052	-171.5013615	2018-01-09 18:00:00	night
2018	01	09	12	007	4.899842	59.34659	1	16.0259513	-173.0592756	2018-01-09 19:00:00	night
2018	01	09	12	008	4.899842	59.34659	1	15.8614271	-171.7753288	2018-01-09 20:00:00	night
2018	01	09	12	009	4.899842	59.34659	1	14.6340079	-170.3575411	2018-01-09 21:00:00	night
2018	01	09	18	004	4.899842	59.34659	1	13.0895093	-177.3417580	2018-01-09 22:00:00	night
2018	01	09	18	005	4.899842	59.34659	1	11.8716349	-177.9696025	2018-01-09 23:00:00	night
2018	01	09	18	006	4.899842	59.34659	1	11.7789357	-174.4242798	2018-01-10	night
2018	01	09	18	007	4.899842	59.34659	1	12.8616257	-178.9956234	2018-01-10 01:00:00	night
2018	01	09	18	008	4.899842	59.34659	1	13.2824812	-179.6146160	2018-01-10 02:00:00	night
2018	01	09	18	009	4.899842	59.34659	1	12.5286503	-178.1703052	2018-01-10 03:00:00	night
2018	01	10	00	004	4.899842	59.34659	1	11.6001177	178.1709884	2018-01-10 04:00:00	night
2018	01	10	00	005	4.899842	59.34659	1	11.6040457	175.0001088	2018-01-10 05:00:00	night
2018	01	10	00	006	4.899842	59.34659	1	12.1815967	170.9582024	2018-01-10 06:00:00	night
2018	01	10	00	007	4.899842	59.34659	1	11.5077119	170.6861933	2018-01-10 07:00:00	night
2018	01	10	00	008	4.899842	59.34659	1	11.3913879	173.1204611	2018-01-10 08:00:00	night
2018	01	10	00	009	4.899842	59.34659	1	11.1711665	168.2475131	2018-01-10 09:00:00	day
2018	01	10	06	004	4.899842	59.34659	1	8.8934929	177.7310467	2018-01-10 10:00:00	day
2018	01	10	06	005	4.899842	59.34659	1	7.5467294	173.8683496	2018-01-10 11:00:00	day
2018	01	10	06	006	4.899842	59.34659	1	7.4881586	171.4861379	2018-01-10 12:00:00	day
2018	01	10	06	007	4.899842	59.34659	1	8.8015472	178.1087966	2018-01-10 13:00:00	day
2018	01	10	06	008	4.899842	59.34659	1	9.5524769	175.8795953	2018-01-10 14:00:00	day
2018	01	10	06	009	4.899842	59.34659	1	9.5866413	-177.4214722	2018-01-10 15:00:00	day
2018	01	10	12	004	4.899842	59.34659	1	8.0150640	165.7097328	2018-01-10 16:00:00	day
2018	01	10	12	005	4.899842	59.34659	1	9.9366639	165.8126537	2018-01-10 17:00:00	night
2018	01	10	12	006	4.899842	59.34659	1	8.4349196	-174.6591545	2018-01-10 18:00:00	night
2018	01	10	12	007	4.899842	59.34659	1	7.6106692	-173.0027910	2018-01-10 19:00:00	night
2018	01	10	12	008	4.899842	59.34659	1	7.0112545	-164.8085532	2018-01-10 20:00:00	night
2018	01	10	12	009	4.899842	59.34659	1	7.3698723	-164.0847166	2018-01-10 21:00:00	night
2018	01	10	18	004	4.899842	59.34659	1	7.5073004	-167.6945907	2018-01-10 22:00:00	night
2018	01	10	18	005	4.899842	59.34659	1	6.5881605	-161.6710527	2018-01-10 23:00:00	night
2018	01	10	18	006	4.899842	59.34659	1	6.5845263	-167.4103314	2018-01-11	night
2018	01	10	18	007	4.899842	59.34659	1	5.8734672	-172.7972563	2018-01-11 01:00:00	night
2018	01	10	18	008	4.899842	59.34659	1	6.3627256	-179.6115558	2018-01-11 02:00:00	night
2018	01	10	18	009	4.899842	59.34659	1	6.8097449	177.9795830	2018-01-11 03:00:00	night
2018	01	11	00	004	4.899842	59.34659	1	4.6239140	-172.3674096	2018-01-11 04:00:00	night
2018	01	11	00	005	4.899842	59.34659	1	4.6363947	-170.4764528	2018-01-11 05:00:00	night
2018	01	11	00	006	4.899842	59.34659	1	5.1039439	-173.4069948	2018-01-11 06:00:00	night
2018	01	11	00	007	4.899842	59.34659	1	5.2128682	173.6704021	2018-01-11 07:00:00	night
2018	01	11	00	008	4.899842	59.34659	1	4.8536608	160.4426809	2018-01-11 08:00:00	night
2018	01	11	00	009	4.899842	59.34659	1	4.7842865	152.4169931	2018-01-11 09:00:00	day
2018	01	11	06	004	4.899842	59.34659	1	3.5364599	163.5006294	2018-01-11 10:00:00	day
2018	01	11	06	005	4.899842	59.34659	1	5.8941499	146.8039313	2018-01-11 11:00:00	day
2018	01	11	06	006	4.899842	59.34659	1	8.3387597	144.2264594	2018-01-11 12:00:00	day
2018	01	11	06	007	4.899842	59.34659	1	8.1622644	149.3325267	2018-01-11 13:00:00	day
2018	01	11	06	008	4.899842	59.34659	1	7.9289852	150.2263216	2018-01-11 14:00:00	day
2018	01	11	06	009	4.899842	59.34659	1	6.8269863	152.0244061	2018-01-11 15:00:00	day
2018	01	11	12	004	4.899842	59.34659	1	5.3359557	135.3091828	2018-01-11 16:00:00	day
2018	01	11	12	005	4.899842	59.34659	1	5.8077994	139.5098115	2018-01-11 17:00:00	night
2018	01	11	12	006	4.899842	59.34659	1	5.3490560	136.6607125	2018-01-11 18:00:00	night
2018	01	11	12	007	4.899842	59.34659	1	5.3597120	132.6959265	2018-01-11 19:00:00	night
2018	01	11	12	008	4.899842	59.34659	1	5.9898146	122.5858535	2018-01-11 20:00:00	night
2018	01	11	12	009	4.899842	59.34659	1	5.7765831	117.2516636	2018-01-11 21:00:00	night
2018	01	11	18	004	4.899842	59.34659	1	4.8033295	115.9971056	2018-01-11 22:00:00	night
2018	01	11	18	005	4.899842	59.34659	1	5.2763220	113.2564748	2018-01-11 23:00:00	night
2018	01	11	18	006	4.899842	59.34659	1	5.0092551	122.3211287	2018-01-12	night
2018	01	11	18	007	4.899842	59.34659	1	5.2978485	118.7117585	2018-01-12 01:00:00	night
2018	01	11	18	008	4.899842	59.34659	1	5.2995359	134.8267943	2018-01-12 02:00:00	night
2018	01	11	18	009	4.899842	59.34659	1	5.6776082	149.3934819	2018-01-12 03:00:00	night
2018	01	12	00	004	4.899842	59.34659	1	3.1937188	151.5852836	2018-01-12 04:00:00	night
2018	01	12	00	005	4.899842	59.34659	1	4.0805409	157.7504558	2018-01-12 05:00:00	night
2018	01	12	00	006	4.899842	59.34659	1	4.1558374	133.9815660	2018-01-12 06:00:00	night
2018	01	12	00	007	4.899842	59.34659	1	6.0645056	106.5843607	2018-01-12 07:00:00	night
2018	01	12	00	008	4.899842	59.34659	1	6.2483883	111.4820770	2018-01-12 08:00:00	night
2018	01	12	00	009	4.899842	59.34659	1	7.0188076	119.1272638	2018-01-12 09:00:00	day
2018	01	12	06	004	4.899842	59.34659	1	7.2986656	130.8408949	2018-01-12 10:00:00	day
2018	01	12	06	005	4.899842	59.34659	1	7.6272136	142.3272473	2018-01-12 11:00:00	day
2018	01	12	06	006	4.899842	59.34659	1	8.0355627	146.8408180	2018-01-12 12:00:00	day
2018	01	12	06	007	4.899842	59.34659	1	7.9312555	149.6161815	2018-01-12 13:00:00	day
2018	01	12	06	008	4.899842	59.34659	1	8.2569357	145.5569539	2018-01-12 14:00:00	day
2018	01	12	06	009	4.899842	59.34659	1	8.4306469	141.8830026	2018-01-12 15:00:00	day
2018	01	12	12	004	4.899842	59.34659	1	9.0680354	144.2715519	2018-01-12 16:00:00	day
2018	01	12	12	005	4.899842	59.34659	1	10.9122867	142.8824386	2018-01-12 17:00:00	night
2018	01	12	12	006	4.899842	59.34659	1	11.7544994	145.1951206	2018-01-12 18:00:00	night
2018	01	12	12	007	4.899842	59.34659	1	12.3326364	147.4819731	2018-01-12 19:00:00	night
2018	01	12	12	008	4.899842	59.34659	1	13.1303604	150.0035097	2018-01-12 20:00:00	night
2018	01	12	12	009	4.899842	59.34659	1	13.9225858	152.1279541	2018-01-12 21:00:00	night
2018	01	12	18	004	4.899842	59.34659	1	14.7822259	153.3569756	2018-01-12 22:00:00	night
2018	01	12	18	005	4.899842	59.34659	1	14.8989881	155.9403231	2018-01-12 23:00:00	night
2018	01	12	18	006	4.899842	59.34659	1	14.3222755	156.5588254	2018-01-13	night
2018	01	12	18	007	4.899842	59.34659	1	13.7052564	157.1532736	2018-01-13 01:00:00	night
2018	01	12	18	008	4.899842	59.34659	1	13.5887786	159.0398975	2018-01-13 02:00:00	night
2018	01	12	18	009	4.899842	59.34659	1	13.0835850	165.0890532	2018-01-13 03:00:00	night
2018	01	13	00	004	4.899842	59.34659	1	14.2992668	168.9232741	2018-01-13 04:00:00	night
2018	01	13	00	005	4.899842	59.34659	1	13.9765520	172.5799365	2018-01-13 05:00:00	night
2018	01	13	00	006	4.899842	59.34659	1	14.5194870	169.5741656	2018-01-13 06:00:00	night
2018	01	13	00	007	4.899842	59.34659	1	14.6902426	168.8339045	2018-01-13 07:00:00	night
2018	01	13	00	008	4.899842	59.34659	1	14.9423999	167.5203911	2018-01-13 08:00:00	night
2018	01	13	00	009	4.899842	59.34659	1	15.5376345	163.1671410	2018-01-13 09:00:00	day
2018	01	13	06	004	4.899842	59.34659	1	16.1430725	164.1704230	2018-01-13 10:00:00	day
2018	01	13	06	005	4.899842	59.34659	1	15.8835407	164.4032695	2018-01-13 11:00:00	day
2018	01	13	06	006	4.899842	59.34659	1	16.0509701	170.3344509	2018-01-13 12:00:00	day
2018	01	13	06	007	4.899842	59.34659	1	15.6047205	173.1261154	2018-01-13 13:00:00	day
2018	01	13	06	008	4.899842	59.34659	1	15.3971503	175.9229202	2018-01-13 14:00:00	day
2018	01	13	06	009	4.899842	59.34659	1	15.5170388	177.2883713	2018-01-13 15:00:00	day
2018	01	13	12	004	4.899842	59.34659	1	16.0919009	173.5778754	2018-01-13 16:00:00	day
2018	01	13	12	005	4.899842	59.34659	1	16.0166148	172.2317570	2018-01-13 17:00:00	night
2018	01	13	12	006	4.899842	59.34659	1	16.4060490	171.4357675	2018-01-13 18:00:00	night
2018	01	13	12	007	4.899842	59.34659	1	17.2677723	171.1021674	2018-01-13 19:00:00	night
2018	01	13	12	008	4.899842	59.34659	1	17.5388952	168.9531306	2018-01-13 20:00:00	night
2018	01	13	12	009	4.899842	59.34659	1	18.3152643	166.0207629	2018-01-13 21:00:00	night
2018	01	13	18	004	4.899842	59.34659	1	18.5224672	166.2492298	2018-01-13 22:00:00	night
2018	01	13	18	005	4.899842	59.34659	1	19.1788278	164.6188864	2018-01-13 23:00:00	night
2018	01	13	18	006	4.899842	59.34659	1	20.0413941	164.0268249	2018-01-14	night
2018	01	13	18	007	4.899842	59.34659	1	20.4519680	162.8750449	2018-01-14 01:00:00	night
2018	01	13	18	008	4.899842	59.34659	1	19.7207400	163.2871338	2018-01-14 02:00:00	night
2018	01	13	18	009	4.899842	59.34659	1	19.0357302	162.0800782	2018-01-14 03:00:00	night
2018	01	14	00	004	4.899842	59.34659	1	20.6547545	162.3017892	2018-01-14 04:00:00	night
2018	01	14	00	005	4.899842	59.34659	1	21.0346741	163.7421311	2018-01-14 05:00:00	night
2018	01	14	00	006	4.899842	59.34659	1	21.1105792	162.9507162	2018-01-14 06:00:00	night
2018	01	14	00	007	4.899842	59.34659	1	20.8708363	160.7192016	2018-01-14 07:00:00	night
2018	01	14	00	008	4.899842	59.34659	1	20.7042343	158.8056390	2018-01-14 08:00:00	night
2018	01	14	00	009	4.899842	59.34659	1	20.8954204	159.1832278	2018-01-14 09:00:00	day
2018	01	14	06	004	4.899842	59.34659	1	21.7527648	157.0197806	2018-01-14 10:00:00	day
2018	01	14	06	005	4.899842	59.34659	1	20.5481785	156.5276769	2018-01-14 11:00:00	day
2018	01	14	06	006	4.899842	59.34659	1	20.9967441	151.3475067	2018-01-14 12:00:00	day
2018	01	14	06	007	4.899842	59.34659	1	21.5883991	157.2067985	2018-01-14 13:00:00	day
2018	01	14	06	008	4.899842	59.34659	1	21.8007643	162.9439942	2018-01-14 14:00:00	day
2018	01	14	06	009	4.899842	59.34659	1	22.6415481	161.7799580	2018-01-14 15:00:00	day
2018	01	14	12	004	4.899842	59.34659	1	24.7034220	162.2096464	2018-01-14 16:00:00	day
2018	01	14	12	005	4.899842	59.34659	1	24.1466227	156.9153474	2018-01-14 17:00:00	night
2018	01	14	12	006	4.899842	59.34659	1	25.3162884	153.5509791	2018-01-14 18:00:00	night
2018	01	14	12	007	4.899842	59.34659	1	25.9680278	154.4226620	2018-01-14 19:00:00	night
2018	01	14	12	008	4.899842	59.34659	1	25.7768069	151.5020262	2018-01-14 20:00:00	night
2018	01	14	12	009	4.899842	59.34659	1	25.6793580	149.9983961	2018-01-14 21:00:00	night
2018	01	14	18	004	4.899842	59.34659	1	26.8545381	150.4216226	2018-01-14 22:00:00	night
2018	01	14	18	005	4.899842	59.34659	1	27.2571826	150.0358583	2018-01-14 23:00:00	night
2018	01	14	18	006	4.899842	59.34659	1	26.8744058	147.7611849	2018-01-15	night
2018	01	14	18	007	4.899842	59.34659	1	26.5822299	148.2086684	2018-01-15 01:00:00	night
2018	01	14	18	008	4.899842	59.34659	1	26.4865370	149.3102670	2018-01-15 02:00:00	night
2018	01	14	18	009	4.899842	59.34659	1	26.7993433	151.3677407	2018-01-15 03:00:00	night
2018	01	15	00	004	4.899842	59.34659	1	27.5549620	153.1485386	2018-01-15 04:00:00	night
2018	01	15	00	005	4.899842	59.34659	1	27.6758186	154.1107691	2018-01-15 05:00:00	night
2018	01	15	00	006	4.899842	59.34659	1	27.7312429	154.2135056	2018-01-15 06:00:00	night
2018	01	15	00	007	4.899842	59.34659	1	27.4266206	153.3142323	2018-01-15 07:00:00	night
2018	01	15	00	008	4.899842	59.34659	1	26.8819883	152.3885944	2018-01-15 08:00:00	night
2018	01	15	00	009	4.899842	59.34659	1	26.0614664	151.5042030	2018-01-15 09:00:00	day
2018	01	15	06	004	4.899842	59.34659	1	25.7451435	152.8841960	2018-01-15 10:00:00	day
2018	01	15	06	005	4.899842	59.34659	1	24.6020439	152.7414082	2018-01-15 11:00:00	day
2018	01	15	06	006	4.899842	59.34659	1	24.3158690	153.0937113	2018-01-15 12:00:00	day
2018	01	15	06	007	4.899842	59.34659	1	24.5262434	154.8698733	2018-01-15 13:00:00	day
2018	01	15	06	008	4.899842	59.34659	1	24.1227224	156.8041173	2018-01-15 14:00:00	day
2018	01	15	06	009	4.899842	59.34659	1	24.2532506	159.2050775	2018-01-15 15:00:00	day
2018	01	15	12	004	4.899842	59.34659	1	24.0355225	164.9149796	2018-01-15 16:00:00	day
2018	01	15	12	005	4.899842	59.34659	1	24.2977011	162.9622633	2018-01-15 17:00:00	night
2018	01	15	12	006	4.899842	59.34659	1	24.7136455	160.6172622	2018-01-15 18:00:00	night
2018	01	15	12	007	4.899842	59.34659	1	22.8498138	157.6168556	2018-01-15 19:00:00	night
2018	01	15	12	008	4.899842	59.34659	1	21.2942527	158.7505213	2018-01-15 20:00:00	night
2018	01	15	12	009	4.899842	59.34659	1	17.1026594	154.8321650	2018-01-15 21:00:00	night
2018	01	15	18	004	4.899842	59.34659	1	3.8645975	92.0063371	2018-01-15 22:00:00	night
2018	01	15	18	005	4.899842	59.34659	1	5.7025229	59.4488330	2018-01-15 23:00:00	night
2018	01	15	18	006	4.899842	59.34659	1	6.6099570	39.9390341	2018-01-16	night
2018	01	15	18	007	4.899842	59.34659	1	6.7529740	43.2634081	2018-01-16 01:00:00	night
2018	01	15	18	008	4.899842	59.34659	1	11.3388040	25.7556320	2018-01-16 02:00:00	night
2018	01	15	18	009	4.899842	59.34659	1	8.6797662	32.0408941	2018-01-16 03:00:00	night
2018	01	16	00	004	4.899842	59.34659	1	3.0165664	94.1393452	2018-01-16 04:00:00	night
2018	01	16	00	005	4.899842	59.34659	1	6.6281634	43.6973014	2018-01-16 05:00:00	night
2018	01	16	00	006	4.899842	59.34659	1	6.7633975	40.7305231	2018-01-16 06:00:00	night
2018	01	16	00	007	4.899842	59.34659	1	7.7798292	46.5248904	2018-01-16 07:00:00	night
2018	01	16	00	008	4.899842	59.34659	1	7.5452479	56.1347244	2018-01-16 08:00:00	night
2018	01	16	00	009	4.899842	59.34659	1	7.5865241	64.3266177	2018-01-16 09:00:00	day
2018	01	16	06	004	4.899842	59.34659	1	8.1667262	56.0724345	2018-01-16 10:00:00	day
2018	01	16	06	005	4.899842	59.34659	1	8.4775097	61.1263866	2018-01-16 11:00:00	day
2018	01	16	06	006	4.899842	59.34659	1	7.9514282	65.1347584	2018-01-16 12:00:00	day
2018	01	16	06	007	4.899842	59.34659	1	8.0467727	72.1139886	2018-01-16 13:00:00	day
2018	01	16	06	008	4.899842	59.34659	1	8.8256674	85.7055248	2018-01-16 14:00:00	day
2018	01	16	06	009	4.899842	59.34659	1	11.3198365	97.8730868	2018-01-16 15:00:00	day
2018	01	16	12	004	4.899842	59.34659	1	10.2999849	45.3436127	2018-01-16 16:00:00	day
2018	01	16	12	005	4.899842	59.34659	1	6.8530570	63.5547588	2018-01-16 17:00:00	night
2018	01	16	12	006	4.899842	59.34659	1	6.9819496	85.8894568	2018-01-16 18:00:00	night
2018	01	16	12	007	4.899842	59.34659	1	7.5663943	92.4498528	2018-01-16 19:00:00	night
2018	01	16	12	008	4.899842	59.34659	1	8.4725081	92.1401082	2018-01-16 20:00:00	night
2018	01	16	12	009	4.899842	59.34659	1	6.9406164	97.9977982	2018-01-16 21:00:00	night
2018	01	16	18	004	4.899842	59.34659	1	5.6476654	99.5368373	2018-01-16 22:00:00	night
2018	01	16	18	005	4.899842	59.34659	1	4.4153163	115.6783497	2018-01-16 23:00:00	night
2018	01	16	18	006	4.899842	59.34659	1	5.9431327	165.7961402	2018-01-17	night
2018	01	16	18	007	4.899842	59.34659	1	3.1720176	115.3482604	2018-01-17 01:00:00	night
2018	01	16	18	008	4.899842	59.34659	1	5.2385771	75.4335044	2018-01-17 02:00:00	night
2018	01	16	18	009	4.899842	59.34659	1	3.6469396	39.6068290	2018-01-17 03:00:00	night
2018	01	17	00	004	4.899842	59.34659	1	6.7749053	97.6281394	2018-01-17 04:00:00	night
2018	01	17	00	005	4.899842	59.34659	1	4.0849000	90.4145670	2018-01-17 05:00:00	night
2018	01	17	00	006	4.899842	59.34659	1	4.8754927	114.8906130	2018-01-17 06:00:00	night
2018	01	17	00	007	4.899842	59.34659	1	4.5994860	101.4728726	2018-01-17 07:00:00	night
2018	01	17	00	008	4.899842	59.34659	1	4.9561683	85.6019305	2018-01-17 08:00:00	night
2018	01	17	00	009	4.899842	59.34659	1	7.1947702	89.2587448	2018-01-17 09:00:00	day
2018	01	17	06	004	4.899842	59.34659	1	4.3293586	69.8834777	2018-01-17 10:00:00	day
2018	01	17	06	005	4.899842	59.34659	1	4.8091760	69.2333545	2018-01-17 11:00:00	day
2018	01	17	06	006	4.899842	59.34659	1	4.8177101	69.7849541	2018-01-17 12:00:00	day
2018	01	17	06	007	4.899842	59.34659	1	3.0710462	85.9119493	2018-01-17 13:00:00	day
2018	01	17	06	008	4.899842	59.34659	1	2.8927019	133.2992630	2018-01-17 14:00:00	day
2018	01	17	06	009	4.899842	59.34659	1	4.0820039	140.1896848	2018-01-17 15:00:00	day
2018	01	17	12	004	4.899842	59.34659	1	5.3642858	162.1341474	2018-01-17 16:00:00	day
2018	01	17	12	005	4.899842	59.34659	1	5.9549056	168.0010148	2018-01-17 17:00:00	night
2018	01	17	12	006	4.899842	59.34659	1	6.5001242	144.5211264	2018-01-17 18:00:00	night
2018	01	17	12	007	4.899842	59.34659	1	7.8507475	141.4250976	2018-01-17 19:00:00	night
2018	01	17	12	008	4.899842	59.34659	1	6.3488387	156.6829937	2018-01-17 20:00:00	night
2018	01	17	12	009	4.899842	59.34659	1	11.0960126	136.1838856	2018-01-17 21:00:00	night
2018	01	17	18	004	4.899842	59.34659	1	10.7041879	170.7477212	2018-01-17 22:00:00	night
2018	01	17	18	005	4.899842	59.34659	1	10.3469511	169.2199432	2018-01-17 23:00:00	night
2018	01	17	18	006	4.899842	59.34659	1	10.8963453	156.4013484	2018-01-18	night
2018	01	17	18	007	4.899842	59.34659	1	10.3800201	150.0602312	2018-01-18 01:00:00	night
2018	01	17	18	008	4.899842	59.34659	1	9.1670922	145.5581252	2018-01-18 02:00:00	night
2018	01	17	18	009	4.899842	59.34659	1	10.3250687	168.7422766	2018-01-18 03:00:00	night
2018	01	18	00	004	4.899842	59.34659	1	11.5337482	143.7698873	2018-01-18 04:00:00	night
2018	01	18	00	005	4.899842	59.34659	1	10.1700432	142.2893723	2018-01-18 05:00:00	night
2018	01	18	00	006	4.899842	59.34659	1	8.1125823	145.0523699	2018-01-18 06:00:00	night
2018	01	18	00	007	4.899842	59.34659	1	8.2650469	165.8761188	2018-01-18 07:00:00	night
2018	01	18	00	008	4.899842	59.34659	1	9.1122189	177.7574042	2018-01-18 08:00:00	night
2018	01	18	00	009	4.899842	59.34659	1	8.3509169	-175.7447002	2018-01-18 09:00:00	day
2018	01	18	06	004	4.899842	59.34659	1	8.9338484	159.1837850	2018-01-18 10:00:00	day
2018	01	18	06	005	4.899842	59.34659	1	8.6901667	154.2589049	2018-01-18 11:00:00	day
2018	01	18	06	006	4.899842	59.34659	1	8.3484500	149.5727055	2018-01-18 12:00:00	day
2018	01	18	06	007	4.899842	59.34659	1	7.4101757	138.1294307	2018-01-18 13:00:00	day
2018	01	18	06	008	4.899842	59.34659	1	7.2929291	143.8787878	2018-01-18 14:00:00	day
2018	01	18	06	009	4.899842	59.34659	1	7.4115492	144.3265131	2018-01-18 15:00:00	day
2018	01	18	12	004	4.899842	59.34659	1	9.8071398	156.5782571	2018-01-18 16:00:00	day
2018	01	18	12	005	4.899842	59.34659	1	11.1249590	159.9917091	2018-01-18 17:00:00	night
2018	01	18	12	006	4.899842	59.34659	1	10.8723850	165.3505233	2018-01-18 18:00:00	night
2018	01	18	12	007	4.899842	59.34659	1	10.4210765	165.6570000	2018-01-18 19:00:00	night
2018	01	18	12	008	4.899842	59.34659	1	10.6888709	163.9131554	2018-01-18 20:00:00	night
2018	01	18	12	009	4.899842	59.34659	1	11.1971062	162.2361899	2018-01-18 21:00:00	night
2018	01	18	18	004	4.899842	59.34659	1	12.3906628	146.8087029	2018-01-18 22:00:00	night
2018	01	18	18	005	4.899842	59.34659	1	12.1418129	149.3914178	2018-01-18 23:00:00	night
2018	01	18	18	006	4.899842	59.34659	1	12.8603041	152.0697867	2018-01-19	night
2018	01	18	18	007	4.899842	59.34659	1	16.0274583	141.1666264	2018-01-19 01:00:00	night
2018	01	18	18	008	4.899842	59.34659	1	12.6698279	140.3344286	2018-01-19 02:00:00	night
2018	01	18	18	009	4.899842	59.34659	1	11.1623294	140.5150321	2018-01-19 03:00:00	night
2018	01	19	00	004	4.899842	59.34659	1	7.5031502	127.9355543	2018-01-19 04:00:00	night
2018	01	19	00	005	4.899842	59.34659	1	6.7430507	76.3395987	2018-01-19 05:00:00	night
2018	01	19	00	006	4.899842	59.34659	1	5.6504858	84.7843349	2018-01-19 06:00:00	night
2018	01	19	00	007	4.899842	59.34659	1	6.5512187	76.1455920	2018-01-19 07:00:00	night
2018	01	19	00	008	4.899842	59.34659	1	7.9134808	147.2285902	2018-01-19 08:00:00	night
2018	01	19	00	009	4.899842	59.34659	1	7.2278275	155.3925625	2018-01-19 09:00:00	day
2018	01	19	06	004	4.899842	59.34659	1	7.7028063	114.0643507	2018-01-19 10:00:00	day
2018	01	19	06	005	4.899842	59.34659	1	9.4722830	92.2941035	2018-01-19 11:00:00	day
2018	01	19	06	006	4.899842	59.34659	1	8.5115568	91.3349017	2018-01-19 12:00:00	day
2018	01	19	06	007	4.899842	59.34659	1	7.7175507	93.2678220	2018-01-19 13:00:00	day
2018	01	19	06	008	4.899842	59.34659	1	8.4885287	123.7257170	2018-01-19 14:00:00	day
2018	01	19	06	009	4.899842	59.34659	1	6.2797672	92.2447879	2018-01-19 15:00:00	day
2018	01	19	12	004	4.899842	59.34659	1	8.2441944	126.2073284	2018-01-19 16:00:00	day
2018	01	19	12	005	4.899842	59.34659	1	9.3041290	139.3502426	2018-01-19 17:00:00	night
2018	01	19	12	006	4.899842	59.34659	1	8.1042349	112.6478467	2018-01-19 18:00:00	night
2018	01	19	12	007	4.899842	59.34659	1	6.9567883	119.0089895	2018-01-19 19:00:00	night
2018	01	19	12	008	4.899842	59.34659	1	5.2673541	67.1092071	2018-01-19 20:00:00	night
2018	01	19	12	009	4.899842	59.34659	1	4.0655429	17.4662087	2018-01-19 21:00:00	night
2018	01	19	18	004	4.899842	59.34659	1	6.6367468	120.7855985	2018-01-19 22:00:00	night
2018	01	19	18	005	4.899842	59.34659	1	5.7261596	115.5552043	2018-01-19 23:00:00	night
2018	01	19	18	006	4.899842	59.34659	1	5.8734475	147.2751482	2018-01-20	night
2018	01	19	18	007	4.899842	59.34659	1	6.1253971	134.4366345	2018-01-20 01:00:00	night
2018	01	19	18	008	4.899842	59.34659	1	6.5147374	160.5238843	2018-01-20 02:00:00	night
2018	01	19	18	009	4.899842	59.34659	1	3.0962265	77.0633940	2018-01-20 03:00:00	night
2018	01	20	00	004	4.899842	59.34659	1	9.4409412	159.7727075	2018-01-20 04:00:00	night
2018	01	20	00	005	4.899842	59.34659	1	8.7151820	166.8076725	2018-01-20 05:00:00	night
2018	01	20	00	006	4.899842	59.34659	1	8.0028583	-179.3445217	2018-01-20 06:00:00	night
2018	01	20	00	007	4.899842	59.34659	1	8.9156312	176.6393322	2018-01-20 07:00:00	night
2018	01	20	00	008	4.899842	59.34659	1	11.0772410	173.1512993	2018-01-20 08:00:00	night
2018	01	20	00	009	4.899842	59.34659	1	9.1993091	174.5115577	2018-01-20 09:00:00	day
2018	01	20	06	004	4.899842	59.34659	1	9.7148796	178.8361397	2018-01-20 10:00:00	day
2018	01	20	06	005	4.899842	59.34659	1	9.8989307	175.9433852	2018-01-20 11:00:00	day
2018	01	20	06	006	4.899842	59.34659	1	10.0851140	167.1553541	2018-01-20 12:00:00	day
2018	01	20	06	007	4.899842	59.34659	1	9.5380578	167.6486173	2018-01-20 13:00:00	day
2018	01	20	06	008	4.899842	59.34659	1	9.1067954	153.3204033	2018-01-20 14:00:00	day
2018	01	20	06	009	4.899842	59.34659	1	4.3611203	113.7079154	2018-01-20 15:00:00	day
2018	01	20	12	004	4.899842	59.34659	1	7.5267865	-144.5043558	2018-01-20 16:00:00	day
2018	01	20	12	005	4.899842	59.34659	1	7.1926017	-142.8058769	2018-01-20 17:00:00	night
2018	01	20	12	006	4.899842	59.34659	1	6.9531536	-142.6579318	2018-01-20 18:00:00	night
2018	01	20	12	007	4.899842	59.34659	1	8.2922497	-155.5460195	2018-01-20 19:00:00	night
2018	01	20	12	008	4.899842	59.34659	1	7.6303832	-163.2326263	2018-01-20 20:00:00	night
2018	01	20	12	009	4.899842	59.34659	1	6.7898170	-156.8921841	2018-01-20 21:00:00	night
2018	01	20	18	004	4.899842	59.34659	1	5.8250746	-153.7208169	2018-01-20 22:00:00	night
2018	01	20	18	005	4.899842	59.34659	1	5.1639130	-153.9784353	2018-01-20 23:00:00	night
2018	01	20	18	006	4.899842	59.34659	1	4.9493690	-155.5670825	2018-01-21	night
2018	01	20	18	007	4.899842	59.34659	1	4.5220850	-155.2963438	2018-01-21 01:00:00	night
2018	01	20	18	008	4.899842	59.34659	1	5.9795480	-176.6027425	2018-01-21 02:00:00	night
2018	01	20	18	009	4.899842	59.34659	1	5.0631831	163.2475668	2018-01-21 03:00:00	night
2018	01	21	00	004	4.899842	59.34659	1	5.7810108	178.3021910	2018-01-21 04:00:00	night
2018	01	21	00	005	4.899842	59.34659	1	5.9259751	168.8080861	2018-01-21 05:00:00	night
2018	01	21	00	006	4.899842	59.34659	1	6.2825524	163.3249743	2018-01-21 06:00:00	night
2018	01	21	00	007	4.899842	59.34659	1	6.2102075	158.8270812	2018-01-21 07:00:00	night
2018	01	21	00	008	4.899842	59.34659	1	8.3001472	160.0546625	2018-01-21 08:00:00	day
2018	01	21	00	009	4.899842	59.34659	1	8.0196236	172.0872644	2018-01-21 09:00:00	day
2018	01	21	06	004	4.899842	59.34659	1	9.3556570	151.4915426	2018-01-21 10:00:00	day
2018	01	21	06	005	4.899842	59.34659	1	10.0442697	149.1263007	2018-01-21 11:00:00	day
2018	01	21	06	006	4.899842	59.34659	1	11.2472281	147.6845702	2018-01-21 12:00:00	day
2018	01	21	06	007	4.899842	59.34659	1	11.4127721	148.5149542	2018-01-21 13:00:00	day
2018	01	21	06	008	4.899842	59.34659	1	11.5327202	152.9029502	2018-01-21 14:00:00	day
2018	01	21	06	009	4.899842	59.34659	1	12.1368241	157.6558491	2018-01-21 15:00:00	day
2018	01	21	12	004	4.899842	59.34659	1	14.0676807	159.3190279	2018-01-21 16:00:00	day
2018	01	21	12	005	4.899842	59.34659	1	14.4513103	165.4177612	2018-01-21 17:00:00	night
2018	01	21	12	006	4.899842	59.34659	1	14.5191306	170.6140899	2018-01-21 18:00:00	night
2018	01	21	12	007	4.899842	59.34659	1	14.6659116	169.5851572	2018-01-21 19:00:00	night
2018	01	21	12	008	4.899842	59.34659	1	14.8969055	169.4362210	2018-01-21 20:00:00	night
2018	01	21	12	009	4.899842	59.34659	1	15.6935513	172.1861371	2018-01-21 21:00:00	night
2018	01	21	18	004	4.899842	59.34659	1	15.8626848	170.8442051	2018-01-21 22:00:00	night
2018	01	21	18	005	4.899842	59.34659	1	16.3076123	170.1204489	2018-01-21 23:00:00	night
2018	01	21	18	006	4.899842	59.34659	1	17.1956250	171.6839018	2018-01-22	night
2018	01	21	18	007	4.899842	59.34659	1	18.5952136	-179.1928573	2018-01-22 01:00:00	night
2018	01	21	18	008	4.899842	59.34659	1	17.5448270	177.9033203	2018-01-22 02:00:00	night
2018	01	21	18	009	4.899842	59.34659	1	15.5814897	175.9535798	2018-01-22 03:00:00	night
2018	01	22	00	004	4.899842	59.34659	1	15.6523485	172.3271662	2018-01-22 04:00:00	night
2018	01	22	00	005	4.899842	59.34659	1	14.6943650	177.7921311	2018-01-22 05:00:00	night
2018	01	22	00	006	4.899842	59.34659	1	18.2035027	172.0038987	2018-01-22 06:00:00	night
2018	01	22	00	007	4.899842	59.34659	1	18.1757340	167.9238713	2018-01-22 07:00:00	night
2018	01	22	00	008	4.899842	59.34659	1	18.1971452	171.5840199	2018-01-22 08:00:00	day
2018	01	22	00	009	4.899842	59.34659	1	17.8802601	172.3487509	2018-01-22 09:00:00	day
2018	01	22	06	004	4.899842	59.34659	1	17.6378864	173.7640624	2018-01-22 10:00:00	day
2018	01	22	06	005	4.899842	59.34659	1	17.9492739	172.4962683	2018-01-22 11:00:00	day
2018	01	22	06	006	4.899842	59.34659	1	18.2317323	169.4550483	2018-01-22 12:00:00	day
2018	01	22	06	007	4.899842	59.34659	1	18.9822495	168.4539752	2018-01-22 13:00:00	day
2018	01	22	06	008	4.899842	59.34659	1	19.2516154	165.5024348	2018-01-22 14:00:00	day
2018	01	22	06	009	4.899842	59.34659	1	19.4372700	161.8796204	2018-01-22 15:00:00	day
2018	01	22	12	004	4.899842	59.34659	1	19.7926073	162.1542532	2018-01-22 16:00:00	day
2018	01	22	12	005	4.899842	59.34659	1	20.1220329	163.5712216	2018-01-22 17:00:00	night
2018	01	22	12	006	4.899842	59.34659	1	20.1519232	163.4238513	2018-01-22 18:00:00	night
2018	01	22	12	007	4.899842	59.34659	1	19.9834655	163.1143190	2018-01-22 19:00:00	night
2018	01	22	12	008	4.899842	59.34659	1	20.1247147	163.2304969	2018-01-22 20:00:00	night
2018	01	22	12	009	4.899842	59.34659	1	20.1244056	160.6701020	2018-01-22 21:00:00	night
2018	01	22	18	004	4.899842	59.34659	1	21.0327507	157.9500765	2018-01-22 22:00:00	night
2018	01	22	18	005	4.899842	59.34659	1	21.0412062	159.2038691	2018-01-22 23:00:00	night
2018	01	22	18	006	4.899842	59.34659	1	21.3018758	157.5640556	2018-01-23	night
2018	01	22	18	007	4.899842	59.34659	1	20.8592561	152.8543480	2018-01-23 01:00:00	night
2018	01	22	18	008	4.899842	59.34659	1	20.4674129	149.4399359	2018-01-23 02:00:00	night
2018	01	22	18	009	4.899842	59.34659	1	19.2640704	144.8258826	2018-01-23 03:00:00	night
2018	01	23	00	004	4.899842	59.34659	1	19.1530160	148.7320822	2018-01-23 04:00:00	night
2018	01	23	00	005	4.899842	59.34659	1	17.8516558	146.8687147	2018-01-23 05:00:00	night
2018	01	23	00	006	4.899842	59.34659	1	16.2435797	145.0494359	2018-01-23 06:00:00	night
2018	01	23	00	007	4.899842	59.34659	1	13.7143291	137.4322184	2018-01-23 07:00:00	night
2018	01	23	00	008	4.899842	59.34659	1	14.2358774	150.6502318	2018-01-23 08:00:00	day
2018	01	23	00	009	4.899842	59.34659	1	15.8535494	147.2481437	2018-01-23 09:00:00	day
2018	01	23	06	004	4.899842	59.34659	1	16.9522517	138.2512481	2018-01-23 10:00:00	day
2018	01	23	06	005	4.899842	59.34659	1	17.6251595	137.2614708	2018-01-23 11:00:00	day
2018	01	23	06	006	4.899842	59.34659	1	17.6058162	140.1258259	2018-01-23 12:00:00	day
2018	01	23	06	007	4.899842	59.34659	1	17.6556132	139.5379686	2018-01-23 13:00:00	day
2018	01	23	06	008	4.899842	59.34659	1	20.1234845	142.0828812	2018-01-23 14:00:00	day
2018	01	23	06	009	4.899842	59.34659	1	20.7354618	151.3844189	2018-01-23 15:00:00	day
2018	01	23	12	004	4.899842	59.34659	1	21.0556052	146.1527383	2018-01-23 16:00:00	day
2018	01	23	12	005	4.899842	59.34659	1	19.6707144	146.4512719	2018-01-23 17:00:00	night
2018	01	23	12	006	4.899842	59.34659	1	16.5721695	133.2502735	2018-01-23 18:00:00	night
2018	01	23	12	007	4.899842	59.34659	1	13.9268232	83.4954200	2018-01-23 19:00:00	night
2018	01	23	12	008	4.899842	59.34659	1	13.2081243	72.7503224	2018-01-23 20:00:00	night
2018	01	23	12	009	4.899842	59.34659	1	11.3691179	67.6400340	2018-01-23 21:00:00	night
2018	01	23	18	004	4.899842	59.34659	1	10.2303996	67.3864326	2018-01-23 22:00:00	night
2018	01	23	18	005	4.899842	59.34659	1	7.0480805	78.1034649	2018-01-23 23:00:00	night
2018	01	23	18	006	4.899842	59.34659	1	8.4452207	100.6383580	2018-01-24	night
2018	01	23	18	007	4.899842	59.34659	1	12.1714967	67.0763696	2018-01-24 01:00:00	night
2018	01	23	18	008	4.899842	59.34659	1	14.1476231	68.4716439	2018-01-24 02:00:00	night
2018	01	23	18	009	4.899842	59.34659	1	12.6565901	58.1227523	2018-01-24 03:00:00	night
2018	01	24	00	004	4.899842	59.34659	1	11.5900349	71.4489546	2018-01-24 04:00:00	night
2018	01	24	00	005	4.899842	59.34659	1	9.3960892	88.0404511	2018-01-24 05:00:00	night
2018	01	24	00	006	4.899842	59.34659	1	8.5546248	125.0690795	2018-01-24 06:00:00	night
2018	01	24	00	007	4.899842	59.34659	1	13.1053917	143.9865607	2018-01-24 07:00:00	night
2018	01	24	00	008	4.899842	59.34659	1	15.2846992	157.5647594	2018-01-24 08:00:00	day
2018	01	24	00	009	4.899842	59.34659	1	18.2382127	170.3434993	2018-01-24 09:00:00	day
2018	01	24	06	004	4.899842	59.34659	1	21.5697437	154.8240240	2018-01-24 10:00:00	day
2018	01	24	06	005	4.899842	59.34659	1	22.6861315	119.1867387	2018-01-24 11:00:00	day
2018	01	24	06	006	4.899842	59.34659	1	21.3313840	111.2931983	2018-01-24 12:00:00	day
2018	01	24	06	007	4.899842	59.34659	1	19.5947064	98.6431819	2018-01-24 13:00:00	day
2018	01	24	06	008	4.899842	59.34659	1	19.3317822	98.1500376	2018-01-24 14:00:00	day
2018	01	24	06	009	4.899842	59.34659	1	20.5625199	102.1282268	2018-01-24 15:00:00	day
2018	01	24	12	004	4.899842	59.34659	1	23.3965941	106.1557066	2018-01-24 16:00:00	day
2018	01	24	12	005	4.899842	59.34659	1	25.8407679	99.3874623	2018-01-24 17:00:00	night
2018	01	24	12	006	4.899842	59.34659	1	24.7007689	93.0642986	2018-01-24 18:00:00	night
2018	01	24	12	007	4.899842	59.34659	1	24.0338840	93.5951020	2018-01-24 19:00:00	night
2018	01	24	12	008	4.899842	59.34659	1	25.2488901	88.5826541	2018-01-24 20:00:00	night
2018	01	24	12	009	4.899842	59.34659	1	20.8363437	66.4832103	2018-01-24 21:00:00	night
2018	01	24	18	004	4.899842	59.34659	1	22.5942527	53.0239180	2018-01-24 22:00:00	night
2018	01	24	18	005	4.899842	59.34659	1	22.8741230	48.3984424	2018-01-24 23:00:00	night
2018	01	24	18	006	4.899842	59.34659	1	21.4183266	46.8549191	2018-01-25	night
2018	01	24	18	007	4.899842	59.34659	1	17.5891519	41.4894393	2018-01-25 01:00:00	night
2018	01	24	18	008	4.899842	59.34659	1	15.5633599	43.1513242	2018-01-25 02:00:00	night
2018	01	24	18	009	4.899842	59.34659	1	12.6445637	45.9111605	2018-01-25 03:00:00	night
2018	01	25	00	004	4.899842	59.34659	1	10.3874280	54.4524110	2018-01-25 04:00:00	night
2018	01	25	00	005	4.899842	59.34659	1	9.4548369	61.7732065	2018-01-25 05:00:00	night
2018	01	25	00	006	4.899842	59.34659	1	9.4210384	65.4828384	2018-01-25 06:00:00	night
2018	01	25	00	007	4.899842	59.34659	1	9.3721344	68.4210265	2018-01-25 07:00:00	night
2018	01	25	00	008	4.899842	59.34659	1	9.1565716	72.7570155	2018-01-25 08:00:00	day
2018	01	25	00	009	4.899842	59.34659	1	9.3846056	79.6993031	2018-01-25 09:00:00	day
2018	01	25	06	004	4.899842	59.34659	1	8.4102995	87.3210225	2018-01-25 10:00:00	day
2018	01	25	06	005	4.899842	59.34659	1	7.9323577	82.2103921	2018-01-25 11:00:00	day
2018	01	25	06	006	4.899842	59.34659	1	8.2410576	80.2945914	2018-01-25 12:00:00	day
2018	01	25	06	007	4.899842	59.34659	1	7.0873820	79.9424857	2018-01-25 13:00:00	day
2018	01	25	06	008	4.899842	59.34659	1	6.5912416	76.2324735	2018-01-25 14:00:00	day
2018	01	25	06	009	4.899842	59.34659	1	9.3876496	92.8743341	2018-01-25 15:00:00	day
2018	01	25	12	004	4.899842	59.34659	1	6.2028210	96.3590097	2018-01-25 16:00:00	day
2018	01	25	12	005	4.899842	59.34659	1	6.7513194	94.1738281	2018-01-25 17:00:00	night
2018	01	25	12	006	4.899842	59.34659	1	7.0907535	90.5397995	2018-01-25 18:00:00	night
2018	01	25	12	007	4.899842	59.34659	1	7.7056774	90.7413497	2018-01-25 19:00:00	night
2018	01	25	12	008	4.899842	59.34659	1	7.9015129	82.9595363	2018-01-25 20:00:00	night
2018	01	25	12	009	4.899842	59.34659	1	7.3355770	76.2068256	2018-01-25 21:00:00	night
2018	01	25	18	004	4.899842	59.34659	1	5.9031912	74.0280766	2018-01-25 22:00:00	night
2018	01	25	18	005	4.899842	59.34659	1	6.1117897	66.3446821	2018-01-25 23:00:00	night
2018	01	25	18	006	4.899842	59.34659	1	6.2296499	62.5283823	2018-01-26	night
2018	01	25	18	007	4.899842	59.34659	1	7.5597494	-31.5396867	2018-01-26 01:00:00	night
2018	01	25	18	008	4.899842	59.34659	1	3.2583950	-57.2796214	2018-01-26 02:00:00	night
2018	01	25	18	009	4.899842	59.34659	1	2.2389933	11.5174589	2018-01-26 03:00:00	night
2018	01	26	00	004	4.899842	59.34659	1	6.6406286	-13.3666086	2018-01-26 04:00:00	night
2018	01	26	00	005	4.899842	59.34659	1	7.4509919	-29.7761425	2018-01-26 05:00:00	night
2018	01	26	00	006	4.899842	59.34659	1	7.2473488	-35.2278193	2018-01-26 06:00:00	night
2018	01	26	00	007	4.899842	59.34659	1	6.4260830	-37.4417768	2018-01-26 07:00:00	night
2018	01	26	00	008	4.899842	59.34659	1	5.8895476	-28.9596897	2018-01-26 08:00:00	day
2018	01	26	00	009	4.899842	59.34659	1	8.1971167	-24.1345108	2018-01-26 09:00:00	day
2018	01	26	06	004	4.899842	59.34659	1	8.3717131	-19.7546015	2018-01-26 10:00:00	day
2018	01	26	06	005	4.899842	59.34659	1	8.1803733	-19.9341562	2018-01-26 11:00:00	day
2018	01	26	06	006	4.899842	59.34659	1	7.7319295	-17.5353860	2018-01-26 12:00:00	day
2018	01	26	06	007	4.899842	59.34659	1	7.0327946	-16.2407374	2018-01-26 13:00:00	day
2018	01	26	06	008	4.899842	59.34659	1	6.9135553	-16.4587773	2018-01-26 14:00:00	day
2018	01	26	06	009	4.899842	59.34659	1	6.2146848	-15.0454058	2018-01-26 15:00:00	day
2018	01	26	12	004	4.899842	59.34659	1	5.4335575	-5.6002185	2018-01-26 16:00:00	day
2018	01	26	12	005	4.899842	59.34659	1	5.6139554	-6.5822481	2018-01-26 17:00:00	night
2018	01	26	12	006	4.899842	59.34659	1	5.4413969	-11.2978957	2018-01-26 18:00:00	night
2018	01	26	12	007	4.899842	59.34659	1	5.2412836	-10.6191839	2018-01-26 19:00:00	night
2018	01	26	12	008	4.899842	59.34659	1	4.2311357	-6.5332101	2018-01-26 20:00:00	night
2018	01	26	12	009	4.899842	59.34659	1	2.9046640	-0.4177728	2018-01-26 21:00:00	night
2018	01	26	18	004	4.899842	59.34659	1	2.3031492	14.2205586	2018-01-26 22:00:00	night
2018	01	26	18	005	4.899842	59.34659	1	2.5140192	43.6562774	2018-01-26 23:00:00	night
2018	01	26	18	006	4.899842	59.34659	1	3.0485045	62.4738967	2018-01-27	night
2018	01	26	18	007	4.899842	59.34659	1	3.2829103	77.6785302	2018-01-27 01:00:00	night
2018	01	26	18	008	4.899842	59.34659	1	3.3320312	92.7087888	2018-01-27 02:00:00	night
2018	01	26	18	009	4.899842	59.34659	1	3.8303239	110.4336129	2018-01-27 03:00:00	night
2018	01	27	00	004	4.899842	59.34659	1	5.1648949	146.1361337	2018-01-27 04:00:00	night
2018	01	27	00	005	4.899842	59.34659	1	7.2482090	163.5339096	2018-01-27 05:00:00	night
2018	01	27	00	006	4.899842	59.34659	1	9.1851021	152.8549201	2018-01-27 06:00:00	night
2018	01	27	00	007	4.899842	59.34659	1	10.3150381	140.0814385	2018-01-27 07:00:00	night
2018	01	27	00	008	4.899842	59.34659	1	12.8612610	148.3742125	2018-01-27 08:00:00	day
2018	01	27	00	009	4.899842	59.34659	1	15.1069747	154.0429814	2018-01-27 09:00:00	day
2018	01	27	06	004	4.899842	59.34659	1	17.3057943	156.6898752	2018-01-27 10:00:00	day
2018	01	27	06	005	4.899842	59.34659	1	19.1016812	156.9900502	2018-01-27 11:00:00	day
2018	01	27	06	006	4.899842	59.34659	1	20.0309711	162.5806386	2018-01-27 12:00:00	day
2018	01	27	06	007	4.899842	59.34659	1	21.8302077	154.4140204	2018-01-27 13:00:00	day
2018	01	27	06	008	4.899842	59.34659	1	23.4950827	150.3573544	2018-01-27 14:00:00	day
2018	01	27	06	009	4.899842	59.34659	1	24.5023032	144.3643216	2018-01-27 15:00:00	day
2018	01	27	12	004	4.899842	59.34659	1	23.8315942	146.0718662	2018-01-27 16:00:00	day
2018	01	27	12	005	4.899842	59.34659	1	21.4963253	150.3778727	2018-01-27 17:00:00	night
2018	01	27	12	006	4.899842	59.34659	1	14.8336960	67.5882174	2018-01-27 18:00:00	night
2018	01	27	12	007	4.899842	59.34659	1	13.8805333	64.6284729	2018-01-27 19:00:00	night
2018	01	27	12	008	4.899842	59.34659	1	10.9109228	72.4885028	2018-01-27 20:00:00	night
2018	01	27	12	009	4.899842	59.34659	1	12.8884210	72.3742657	2018-01-27 21:00:00	night
2018	01	27	18	004	4.899842	59.34659	1	13.1769255	83.3031753	2018-01-27 22:00:00	night
2018	01	27	18	005	4.899842	59.34659	1	17.1594634	75.7939829	2018-01-27 23:00:00	night
2018	01	27	18	006	4.899842	59.34659	1	17.0041495	86.2872620	2018-01-28	night
2018	01	27	18	007	4.899842	59.34659	1	20.0316479	68.3023182	2018-01-28 01:00:00	night
2018	01	27	18	008	4.899842	59.34659	1	20.5304548	66.2561495	2018-01-28 02:00:00	night
2018	01	27	18	009	4.899842	59.34659	1	18.4324806	51.0751755	2018-01-28 03:00:00	night
2018	01	28	00	004	4.899842	59.34659	1	20.1836332	44.3028433	2018-01-28 04:00:00	night
2018	01	28	00	005	4.899842	59.34659	1	22.0253768	35.9643084	2018-01-28 05:00:00	night
2018	01	28	00	006	4.899842	59.34659	1	20.0802583	30.2878395	2018-01-28 06:00:00	night
2018	01	28	00	007	4.899842	59.34659	1	21.3609452	26.2479575	2018-01-28 07:00:00	night
2018	01	28	00	008	4.899842	59.34659	1	20.3871902	27.8952109	2018-01-28 08:00:00	day
2018	01	28	00	009	4.899842	59.34659	1	18.4918332	30.6081463	2018-01-28 09:00:00	day
2018	01	28	06	004	4.899842	59.34659	1	19.8210545	31.3657977	2018-01-28 10:00:00	day
2018	01	28	06	005	4.899842	59.34659	1	18.9524321	32.5667622	2018-01-28 11:00:00	day
2018	01	28	06	006	4.899842	59.34659	1	17.7205575	35.8176537	2018-01-28 12:00:00	day
2018	01	28	06	007	4.899842	59.34659	1	17.4856014	36.6447362	2018-01-28 13:00:00	day
2018	01	28	06	008	4.899842	59.34659	1	16.8771767	31.8087050	2018-01-28 14:00:00	day
2018	01	28	06	009	4.899842	59.34659	1	15.4540102	29.0666297	2018-01-28 15:00:00	day
2018	01	28	12	004	4.899842	59.34659	1	14.0014410	38.5866831	2018-01-28 16:00:00	day
2018	01	28	12	005	4.899842	59.34659	1	12.6631745	38.0738659	2018-01-28 17:00:00	night
2018	01	28	12	006	4.899842	59.34659	1	11.5296130	38.2497770	2018-01-28 18:00:00	night
2018	01	28	12	007	4.899842	59.34659	1	9.9806951	41.0191743	2018-01-28 19:00:00	night
2018	01	28	12	008	4.899842	59.34659	1	9.2245936	50.4697309	2018-01-28 20:00:00	night
2018	01	28	12	009	4.899842	59.34659	1	8.4669090	54.5066865	2018-01-28 21:00:00	night
2018	01	28	18	004	4.899842	59.34659	1	8.4106176	62.2474045	2018-01-28 22:00:00	night
2018	01	28	18	005	4.899842	59.34659	1	7.7008238	66.8281676	2018-01-28 23:00:00	night
2018	01	28	18	006	4.899842	59.34659	1	6.0160924	66.4924010	2018-01-29	night
2018	01	28	18	007	4.899842	59.34659	1	7.7102102	94.2283116	2018-01-29 01:00:00	night
2018	01	28	18	008	4.899842	59.34659	1	3.2594727	142.7707354	2018-01-29 02:00:00	night
2018	01	28	18	009	4.899842	59.34659	1	4.9193290	175.7509034	2018-01-29 03:00:00	night
2018	01	29	00	004	4.899842	59.34659	1	3.5698480	169.7516239	2018-01-29 04:00:00	night
2018	01	29	00	005	4.899842	59.34659	1	2.9898434	132.4224119	2018-01-29 05:00:00	night
2018	01	29	00	006	4.899842	59.34659	1	3.6122234	65.9499280	2018-01-29 06:00:00	night
2018	01	29	00	007	4.899842	59.34659	1	8.9344314	27.5043870	2018-01-29 07:00:00	night
2018	01	29	00	008	4.899842	59.34659	1	9.7710512	27.1541137	2018-01-29 08:00:00	day
2018	01	29	00	009	4.899842	59.34659	1	10.0817273	35.7621661	2018-01-29 09:00:00	day
2018	01	29	06	004	4.899842	59.34659	1	13.2283604	26.2923036	2018-01-29 10:00:00	day
2018	01	29	06	005	4.899842	59.34659	1	13.2546648	27.1984694	2018-01-29 11:00:00	day
2018	01	29	06	006	4.899842	59.34659	1	15.2466165	20.3397524	2018-01-29 12:00:00	day
2018	01	29	06	007	4.899842	59.34659	1	17.1915392	2.7085950	2018-01-29 13:00:00	day
2018	01	29	06	008	4.899842	59.34659	1	13.3891050	-3.4975688	2018-01-29 14:00:00	day
2018	01	29	06	009	4.899842	59.34659	1	14.2573594	-11.9135948	2018-01-29 15:00:00	day
2018	01	29	12	004	4.899842	59.34659	1	14.2753749	13.4085638	2018-01-29 16:00:00	day
2018	01	29	12	005	4.899842	59.34659	1	14.6860752	11.7334989	2018-01-29 17:00:00	night
2018	01	29	12	006	4.899842	59.34659	1	12.6907010	4.1794816	2018-01-29 18:00:00	night
2018	01	29	12	007	4.899842	59.34659	1	19.2253210	13.4623727	2018-01-29 19:00:00	night
2018	01	29	12	008	4.899842	59.34659	1	12.3058515	27.7378734	2018-01-29 20:00:00	night
2018	01	29	12	009	4.899842	59.34659	1	11.1911289	-12.8467495	2018-01-29 21:00:00	night
2018	01	29	18	004	4.899842	59.34659	1	13.6002162	9.8669139	2018-01-29 22:00:00	night
2018	01	29	18	005	4.899842	59.34659	1	15.1860840	6.3444004	2018-01-29 23:00:00	night
2018	01	29	18	006	4.899842	59.34659	1	15.0134375	4.2618028	2018-01-30	night
2018	01	29	18	007	4.899842	59.34659	1	15.2255134	10.4932775	2018-01-30 01:00:00	night
2018	01	29	18	008	4.899842	59.34659	1	13.3457023	14.8194487	2018-01-30 02:00:00	night
2018	01	29	18	009	4.899842	59.34659	1	12.7260404	17.2508803	2018-01-30 03:00:00	night
2018	01	30	00	004	4.899842	59.34659	1	10.9430412	23.5083516	2018-01-30 04:00:00	night
2018	01	30	00	005	4.899842	59.34659	1	11.1119667	28.6073829	2018-01-30 05:00:00	night
2018	01	30	00	006	4.899842	59.34659	1	11.7577002	32.6972958	2018-01-30 06:00:00	night
2018	01	30	00	007	4.899842	59.34659	1	12.2163235	33.9070929	2018-01-30 07:00:00	night
2018	01	30	00	008	4.899842	59.34659	1	11.0398346	36.7745349	2018-01-30 08:00:00	day
2018	01	30	00	009	4.899842	59.34659	1	7.3595933	51.3327152	2018-01-30 09:00:00	day
2018	01	30	06	004	4.899842	59.34659	1	9.0182329	56.7289771	2018-01-30 10:00:00	day
2018	01	30	06	005	4.899842	59.34659	1	9.2010398	66.6998634	2018-01-30 11:00:00	day
2018	01	30	06	006	4.899842	59.34659	1	8.4225044	85.3010616	2018-01-30 12:00:00	day
2018	01	30	06	007	4.899842	59.34659	1	13.8529138	83.9016746	2018-01-30 13:00:00	day
2018	01	30	06	008	4.899842	59.34659	1	12.2072715	95.4085511	2018-01-30 14:00:00	day
2018	01	30	06	009	4.899842	59.34659	1	14.2777318	64.0856912	2018-01-30 15:00:00	day
2018	01	30	12	004	4.899842	59.34659	1	12.7814244	75.1134194	2018-01-30 16:00:00	day
2018	01	30	12	005	4.899842	59.34659	1	12.0669234	74.3120918	2018-01-30 17:00:00	night
2018	01	30	12	006	4.899842	59.34659	1	13.1070896	74.5164101	2018-01-30 18:00:00	night
2018	01	30	12	007	4.899842	59.34659	1	13.3919368	71.7505815	2018-01-30 19:00:00	night
2018	01	30	12	008	4.899842	59.34659	1	11.1253200	94.0413524	2018-01-30 20:00:00	night
2018	01	30	12	009	4.899842	59.34659	1	12.0190175	103.7781569	2018-01-30 21:00:00	night
2018	01	30	18	004	4.899842	59.34659	1	14.5355985	105.3285124	2018-01-30 22:00:00	night
2018	01	30	18	005	4.899842	59.34659	1	14.9985192	108.5082750	2018-01-30 23:00:00	night
2018	01	30	18	006	4.899842	59.34659	1	15.5342149	108.6641071	2018-01-31	night
2018	01	30	18	007	4.899842	59.34659	1	15.4842529	104.8910501	2018-01-31 01:00:00	night
2018	01	30	18	008	4.899842	59.34659	1	15.7418564	112.3857991	2018-01-31 02:00:00	night
2018	01	30	18	009	4.899842	59.34659	1	16.0881100	121.0316034	2018-01-31 03:00:00	night
2018	01	31	00	004	4.899842	59.34659	1	14.8705397	125.7751532	2018-01-31 04:00:00	night
2018	01	31	00	005	4.899842	59.34659	1	14.5846657	123.3265154	2018-01-31 05:00:00	night
2018	01	31	00	006	4.899842	59.34659	1	14.1807899	119.5347898	2018-01-31 06:00:00	night
2018	01	31	00	007	4.899842	59.34659	1	12.8809280	121.7407326	2018-01-31 07:00:00	night
2018	01	31	00	008	4.899842	59.34659	1	12.4730525	115.9935691	2018-01-31 08:00:00	day
2018	01	31	00	009	4.899842	59.34659	1	12.1304744	113.4228756	2018-01-31 09:00:00	day
2018	01	31	06	004	4.899842	59.34659	1	9.4694410	117.7136616	2018-01-31 10:00:00	day
2018	01	31	06	005	4.899842	59.34659	1	8.1549583	97.6770633	2018-01-31 11:00:00	day
2018	01	31	06	006	4.899842	59.34659	1	5.5860954	59.2134733	2018-01-31 12:00:00	day
2018	01	31	06	007	4.899842	59.34659	1	6.2798789	59.5533932	2018-01-31 13:00:00	day
2018	01	31	06	008	4.899842	59.34659	1	5.7034571	101.6177989	2018-01-31 14:00:00	day
2018	01	31	06	009	4.899842	59.34659	1	8.6126216	64.2852564	2018-01-31 15:00:00	day
2018	01	31	12	004	4.899842	59.34659	1	9.2665738	72.5308502	2018-01-31 16:00:00	day
2018	01	31	12	005	4.899842	59.34659	1	8.8023354	70.8819353	2018-01-31 17:00:00	night
2018	01	31	12	006	4.899842	59.34659	1	8.0099798	81.1108250	2018-01-31 18:00:00	night
2018	01	31	12	007	4.899842	59.34659	1	9.0374472	93.0484904	2018-01-31 19:00:00	night
2018	01	31	12	008	4.899842	59.34659	1	8.4081310	116.6073202	2018-01-31 20:00:00	night
2018	01	31	12	009	4.899842	59.34659	1	7.6869268	129.5785468	2018-01-31 21:00:00	night
2018	01	31	18	004	4.899842	59.34659	1	7.6348544	142.9447568	2018-01-31 22:00:00	night
2018	01	31	18	005	4.899842	59.34659	1	5.2573967	141.2016131	2018-01-31 23:00:00	night
2018	01	31	18	006	4.899842	59.34659	1	6.0571270	175.8738969	2018-02-01	night
2018	01	31	18	007	4.899842	59.34659	1	9.7014436	166.4958404	2018-02-01 01:00:00	night
2018	01	31	18	008	4.899842	59.34659	1	9.7242861	179.7773042	2018-02-01 02:00:00	night
2018	01	31	18	009	4.899842	59.34659	1	8.1432931	-174.1726553	2018-02-01 03:00:00	night

Hourly analysis

Use average hourly data directly from NORA3.

Show the code

ggplot(data = wind_100m, aes(x = time, y = u_mag)) +
  geom_point() +
  # geom_smooth(span = 0.3, se = TRUE) +
  # Cut-in
  geom_hline(yintercept = 3.5, col = "#009E73", linetype = "dashed") + 
  geom_text(x = as_datetime("2018-01-01 04:00:00"),
            y = 4,
            label = "Cut-in",
            colour = "#009E73") +
  # Cut-off
  geom_hline(yintercept = 25, col = "#D55E00", linetype = "dashed") +
  geom_text(x = as_datetime("2018-01-01 04:00:00"),
            y = 25.5,
            label = "Cut-off",
            colour = "#D55E00") +
  # Format labels and title
  labs(x = "Time",
       y = "u [m/s]",
       title = "Hourly wind speed at 100 m height", 
       subtitle = "Coordinates (WGS84): Long = 4.899842, lat = 59.346588",
       caption = "Data: NORA3") +
  coord_cartesian(ylim = c(0,30)) +
  theme_bw()

Histogram

Show the code

# Plot histogram 
ggplot() +
  geom_histogram(data = wind_100m, aes(x = u_mag, y =..density..),
                 binwidth = 0.5, 
                 fill = "#D55E00", 
                 colour = "grey") +
  # Format labels and title
  labs(x = "u [m/s]",
       title = "Histogram of hourly wind speed at 100 m height", 
       subtitle = "Point coordinates (WGS84): Long = 4.899842, lat = 59.346588",
       caption = "Data: NORA3") +
  coord_cartesian(xlim = c(0, 65)) +
  theme_bw()

Fit a Weibull distribution

Show the code

# Fit weibul distribution
fit <- fitdistrplus::fitdist(wind_100m$u_mag,"weibull")

# Put parameter in a table (inset element)
weibull_param <- tibble(param = rownames(as.data.frame(fit$estimate)),
                        estimate = fit$estimate,
                        std_Error = fit$sd)

# Generate values for addind the density curve
fit_values <- rweibull(1000000, shape = fit$estimate[1], scale = fit$estimate[2]) |> 
  as_tibble()

# Plot histogram together with the weibull distribution 
ggplot() +
  geom_histogram(data = wind_100m, aes(x = u_mag, y =..density..),
                 binwidth = 0.5, 
                 fill = "#D55E00", 
                 colour = "grey") +
  # Addd Weibull
  geom_density(data = fit_values,
               aes(x = value, y = ..density..),
               colour = "#0072B2",
               linewidth = 1) +
  # Format labels and title
  labs(x = "u [m/s]",
       title = "Weibull distribution of hourly wind speed at 100 m height", 
       subtitle = "Point coordinates (WGS84): Long = 4.899842, lat = 59.346588",
       caption = "Data: NORA3") +
  coord_cartesian(xlim = c(0, 65)) +
  theme_bw() +
  # Add inset table
  inset_element(gt(weibull_param), 0.60, 0.75, 0.95, 0.95)

Day-night variation in the wind speed distribution

Show the code

# Separate by day-night and fit a weibull for each group 
fit_weibull <- function(df) { fitdistrplus::fitdist(df$u_mag,"weibull") }

group_names <- wind_100m |> 
  group_by(day_night) |>
  group_keys() |>
  pull(1)

fit <- wind_100m |> 
  group_split(day_night) |> 
  set_names(group_names) |> 
  map(.f = fit_weibull)

# Put parameter in a table (inset element)
weibull_param <- tibble(param = c("shape", "scale"),
                        group = "day",
                        estimate = fit$day[[1]],
                        std_error = fit$day[[3]]
  ) |> 
  bind_rows(
    tibble(param = c("shape", "scale"),
           group = "night",
           estimate = fit$night[[1]],
           std_error = fit$night[[3]])
  )


# Generate values for adding the weibull curve
fit_values <- tibble(
  day = rweibull(1000000, shape = fit$day[[1]][1], scale = fit$day[[1]][2]),
  night = rweibull(1000000, shape = fit$night[[1]][1], scale = fit$night[[1]][2])
) |> 
  pivot_longer(cols = everything(),
               names_to = "day_night",
               values_to = "value"
               )

# Plot histograms
ggplot() +
  geom_histogram(data = wind_100m, aes(x = u_mag, y =..density..),
                 binwidth = 0.5, 
                 fill = "#D55E00", 
                 colour = "grey") +
  # Addd Weibull
  geom_density(data = fit_values,
               aes(x = value, y = ..density..),
               colour = "#0072B2",
               linewidth = 1) +
  # Format labels and title
  labs(x = "u [m/s]",
       title = "Weibull distribution of hourly wind speed at 100 m height", 
       subtitle = "Point coordinates (WGS84): Long = 4.899842, lat = 59.346588",
       caption = "Data: NORA3") +
  coord_cartesian(xlim = c(0, 65)) +
  facet_grid(~day_night) +
  theme_bw() +
  # Add inset table
  inset_element(gt(filter(weibull_param, group == "day") |> select(-group)),
                0.25, 0.8, 0.45, 0.95) +
  inset_element(gt(filter(weibull_param, group == "night") |> select(-group)),
                0.75, 0.8, 0.95, 0.95)

Figure 8: Histogram and weibull parameters of wind speed

Time bellow/above thresholds:

Show the code

# Time in production 
wind_100m <- wind_100m |> 
  select(time, u_mag, u_dir) |> 
  mutate(production = case_when(
    u_mag < 3.5 ~ FALSE,
    u_mag >  25 ~ FALSE,
    .default = TRUE)
    ) 

wind_100m |> 
  summarise(
    total_hours = n(),
    total_prod = sum(production),
    perc = 100 * mean(production)
  ) |> 
  gt() |> 
  fmt_number(columns = perc,
             decimals = 1)

Table 1: Percentage of time that a wind turbine is producing energy based on average hourly wind speed data (cut-in = 3.5 m/s, and cut-off = 25 m/s)

total_hours	total_prod	perc
744	688	92.5

Downscale to 10-min resolution

Create simulated data (n = 1000) based on NORA3 hourly data. Simulated data follow a Weibull distribution with the same global shape of day/night but mean values of the hourly data.

Show the code

set.seed(36)
n_sim = 1000

# Initial data frame  
sim_wind_100m <- tibble(time = seq(ymd_hm("2018-01-01 04:00"),
                                   ymd_hm("2018-02-01 03:50"), 
                                   by = "10 mins")) |> 
  # Add mean values and simulate weibull 
  left_join(wind_100m) |> 
  dplyr::select(time, u_mag) |> 
  fill(u_mag) |> 
  # Add columns indicatind day/night time
  mutate(day_night = ifelse(is_daytime(time, tz = "Europe/Oslo"), "day", "night")) 

# Simulations day
wd_shape <- weibull_param |> filter(group == "day", param == "shape") |> pull(estimate)
sim_wind_100m_day <- filter(sim_wind_100m, day_night == "day")
sim_day <- matrix(NA, length(sim_wind_100m_day$u_mag), n_sim)
u_mag <- sim_wind_100m_day$u_mag

for(i in 1:n_sim) {
  for(j in seq_along(u_mag)) {
    sim_day[j, i] = rweibull(1, shape = wd_shape, scale = u_mag[j] / gamma(1+(1/wd_shape)))
  }
}

sim_day <- sim_day |> 
  as_tibble() |> 
  rename_with(~ paste0("sim_", .)) |> 
  rename_with(~ gsub("V", "", .x, fixed = TRUE))

sim_wind_100m_day <- sim_wind_100m_day |> 
  bind_cols(sim_day)

# Simulations night
wd_shape <- weibull_param |> filter(group == "night", param == "shape") |> pull(estimate)
sim_wind_100m_night <- filter(sim_wind_100m, day_night == "night")
sim_night <- matrix(NA, length(sim_wind_100m_night$u_mag), n_sim)
u_mag <- sim_wind_100m_night$u_mag
for(i in 1:n_sim) {
  for(j in seq_along(u_mag)) {
    sim_night[j, i] = rweibull(1, shape = wd_shape, scale = u_mag[j] / gamma(1+(1/wd_shape)))
  }
}

sim_night <- sim_night |> 
  as_tibble() |> 
  rename_with(~ paste0("sim_", .)) |> 
  rename_with(~ gsub("V", "", .x, fixed = TRUE))

sim_wind_100m_night <- sim_wind_100m_night |> 
  bind_cols(sim_night)

# Merge data
sim_wind_100m <- sim_wind_100m_day |> 
  bind_rows(sim_wind_100m_night) |> 
  arrange(time)

Show the code

# Plot example of 4 simulations
sim_wind_100m |> 
  select("sim_1", "sim_2", "sim_3", "sim_4") |> 
  pivot_longer(cols = starts_with("sim"))  |> 
  ggplot(aes(x = value)) +
  geom_histogram(aes(y =..density..),
                 bins = 50,
                 fill = "#D55E00",
                 colour = "grey") +
  geom_density() +
  labs(title = "Wind distribution in simulated data",
       subtitle = "Example of the first four simulations",
       x = "u [m/s]") +
  facet_wrap(~ name, ncol = 2) +
  theme_bw()

Show the code

# Exampla first four simulations
sim_wind_100m |> 
  select("time","u_mag", "sim_1", "sim_2", "sim_3", "sim_4") |> 
  pivot_longer(cols = starts_with("sim")) |> 
  ggplot() +
  geom_point(aes(x = time, y = value),
             colour = "#0072B2",
             size = 0.6,
             alpha = 0.5) + 
  # Hourly points
  geom_line(data = wind_100m, aes(x = time, y = u_mag), col = "red") +
  # Cut-in
  geom_hline(yintercept = 3.5, col = "#009E73", linetype = "dashed") +
  # Cut-off
  geom_hline(yintercept = 25, col = "#D55E00", linetype = "dashed") +
  # Format labels and title
  labs(x = "Time",
       y = "u [m/s]",
       title = "Simulated wind speed at 100 m height (10 min resolution)", 
       subtitle = "Exampla of the first four simulations",
       caption = "Red line indicates hourly mean value from NORA3") +
  facet_wrap(~ name, ncol = 2) +
  theme_bw()

Figure 10: Hourly wind speed (m/s) of simntetic data

Show the code

sim_wind_100m |> 
  pivot_longer(cols = starts_with("sim")) |> 
  ggplot() +
  # geom_point(aes(x = time, y = value),
  #            colour = "#0072B2",
  #            size = 0.01,
  #            alpha = 0.05) + 
  geom_hex(aes(x = time, y = value), bins = 500) +
  viridis:: scale_fill_viridis(trans = "log10") +
  # Hourly points
  geom_line(data = wind_100m,
            aes(x = time, y = u_mag), 
            col = "red",
            alpha = 0.5) +
  # Cut-in
  geom_hline(yintercept = 3.5, col = "#009E73", linetype = "dashed") +
  # Cut-off
  geom_hline(yintercept = 25, col = "#D55E00", linetype = "dashed") +
  # Format labels and title
  labs(x = "Time",
       y = "u [m/s]",
       title = "Simulated wind speed at 100 m height (10 min resolution)", 
       subtitle = "All simulations",
       caption = "Red line indicates hourly mean value from NORA3") +
  theme_bw()

Hourly average vs. NORA3

Show the code

sim_wind_100m |>  
  mutate(date_hour = format(time, "%Y-%m-%d %H")) |> 
  relocate(date_hour, .after = time) |> 
  pivot_longer(cols = starts_with("sim")) |> 
  group_by(date_hour) |> 
  summarise(
    n = n(),
    time = first(time),
    u_mag = first(u_mag),
    sim_mean = mean(value)
    ) |> 
  ungroup() |> 
  ggplot() +
  geom_point(aes(x = u_mag, y = sim_mean), alpha = 0.5) +
  labs(title = "Hourly wind speed [m/s]",
       y = "Simulated data",
       x = "NORA3 data") +
  geom_abline(intercept = 0, slope = 1, col = "red") +
  coord_equal() + 
  theme_bw()

Figure 11: Relationship between the hourly wind speed calculated in the simulated data and NORA3 data values

Show the code

sim_wind_100m_hour <- sim_wind_100m |>  
  mutate(date_hour = format(time, "%Y-%m-%d %H")) |> 
  relocate(date_hour, .after = time) |> 
  pivot_longer(cols = starts_with("sim")) |> 
  tidytable::group_by(date_hour,name) |> 
  tidytable::summarise(
    n = n(),
    time = first(time),
    u_mag = first(u_mag),
    sim_mean = mean(value)
  ) |> 
  tidytable::ungroup() 


sim_wind_100m_hour |> 
  ggplot() +
  geom_hex(aes(x = u_mag, y = sim_mean), bins = 120) +
  viridis::scale_fill_viridis(trans = "log10") +
  labs(title = "Hourly wind speed [m/s]",
       y = "Simulated data",
       x = "NORA3 data") +
  geom_abline(intercept = 0, slope = 1, col = "red") +
  coord_equal() +
  theme_bw()

Show the code

# Time in production 
sim_wind_100m_hour <- sim_wind_100m_hour |> 
  mutate(production = case_when(
    sim_mean < 3.5 ~ FALSE,
    sim_mean >  25 ~ FALSE,
    .default = TRUE)
    ) 

# Time in production
sim_wind_100m_hour_prod <- sim_wind_100m_hour |> 
  group_by(name) |> 
  summarise(
    total_hours = n(),
    total_prod = sum(production),
    perc = 100 * mean(production)
  ) |> 
  ungroup()


# Plot percentage
sim_wind_100m_hour_prod |> 
  ggplot(aes(x = perc)) +
  geom_histogram(aes(y =..density..),
                 bins = 40,
                 fill = "#D55E00",
                 colour = "grey") +
  geom_density() +
  labs(x = "Perc. [%]",
       title = "Histogram of the percentage the time in production",
       subtitle = "Based on hourly means of simulated data",
       caption = "Wind speed between cut-in = 3.5 m/s and cut-off = 25 m/s") +
  theme_bw()

Percentage of time that a wind generator is producing energy in the 10 minute simulated data:

Show the code

prod_10min <- sim_wind_100m |> 
  pivot_longer(cols = starts_with("sim")) |> 
  group_by(name) |>
  mutate(production = case_when(
    value < 3.5 ~ FALSE,
    value >  25 ~ FALSE,
    .default = TRUE)
    ) |> 
  summarise(
    total_hours = n() * 10/60,
    total_prod = sum(production) * 10/60,
    perc = 100 * mean(production)
    ) |> 
  ungroup()


# Plot
prod_10min |> 
  ggplot(aes(x = perc)) +
  geom_histogram(aes(y =..density..),
                 bins = 40,
                 fill = "#D55E00",
                 colour = "grey") +
  geom_density() +
  labs(x = "Perc. [%]",
       title = "Histogram of the percentage of hours in production",
       subtitle = "Wind speed between cut-in = 3.5 m/s and cut-off = 25 m/s") +
  theme_bw()

Figure 12: Percentage of time that a wind generator is producing energy based on simulated data (cut-in = 3 m/s and cut-off = 25 m/s)

Show the code

prod_10min |> 
  select("total_hours", "total_prod", "perc") |> 
  purrr::map_dfr(.f = summary, .id = "value") |> 
  mutate_at(vars(-value), as.numeric) |> 
  gt() |> 
  fmt_number(columns = vars(-value),
             rows = c(1,2),
             decimals = 0) |> 
  fmt_number(columns = vars(-value),
             rows = 3,
             decimals = 1)

Table 2: Percentage of time that a wind generator is producing energy based on simulated data (cut-in = 3 m/s and cut-off = 25 m/s)

value	Min.	1st Qu.	Median	Mean	3rd Qu.	Max.
total_hours	744	744	744	744	744	744
total_prod	555	565	568	568	571	580
perc	74.6	75.9	76.3	76.3	76.7	77.9

Analyse variability in the data.

Solbrekke, Ida Marie, and Asgeir Sorteberg. 2022. “NORA3-WP: A High-Resolution Offshore Wind Power Dataset for the Baltic, North, Norwegian, and Barents Seas.” Scientific Data 9 (1): 362. https://doi.org/10.1038/s41597-022-01451-x.